diff --git a/qiskit_machine_learning/optimizers/__init__.py b/qiskit_machine_learning/optimizers/__init__.py
index df5653478..f5ccea049 100644
--- a/qiskit_machine_learning/optimizers/__init__.py
+++ b/qiskit_machine_learning/optimizers/__init__.py
@@ -122,6 +122,7 @@
 """
 
 from .adam_amsgrad import ADAM
+from .nadam import NAdam
 from .aqgd import AQGD
 from .cg import CG
 from .cobyla import COBYLA
@@ -157,6 +158,7 @@
     "OptimizerResult",
     "Minimizer",
     "ADAM",
+    "NAdam",
     "AQGD",
     "CG",
     "COBYLA",
diff --git a/qiskit_machine_learning/optimizers/nadam.py b/qiskit_machine_learning/optimizers/nadam.py
new file mode 100644
index 000000000..12acfc005
--- /dev/null
+++ b/qiskit_machine_learning/optimizers/nadam.py
@@ -0,0 +1,148 @@
+# This code is part of a Qiskit project.
+#
+# (C) Copyright IBM 2019, 2025.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""NAdam Optimizer"""
+
+from __future__ import annotations
+import os
+import numpy as np
+from typing import Callable
+from .optimizer import Optimizer, OptimizerResult, POINT, OptimizerSupportLevel
+
+
+class NAdam(Optimizer):
+    """NAdam optimizer (Nesterov-accelerated Adaptive Moment Estimation)."""
+
+    def __init__(
+        self,
+        maxiter: int = 200,
+        tol: float = 1e-6,
+        lr: float = 0.001,
+        beta_1: float = 0.9,
+        beta_2: float = 0.999,
+        eps: float = 1e-8,
+        noise_factor: float = 1e-8,
+        callback: Callable | None = None,
+        snapshot_dir: str | None = None,
+    ) -> None:
+        """Initialize NAdam optimizer."""
+        super().__init__()
+        self.maxiter = maxiter
+        self.tol = tol
+        self.lr = lr
+        self.beta_1 = beta_1
+        self.beta_2 = beta_2
+        self.eps = eps
+        self.noise_factor = noise_factor
+        self.callback = callback
+        self.snapshot_dir = snapshot_dir
+
+        # Internal state
+        self._m = None
+        self._v = None
+        self._t = 0
+
+    def get_support_level(self):
+        """Return the support level for NAdam optimizer."""
+        return {
+            "gradient": OptimizerSupportLevel.ignored,
+            "bounds": OptimizerSupportLevel.ignored,
+            "initial_point": OptimizerSupportLevel.required,
+        }
+
+    @property
+    def settings(self):
+        """Return optimizer settings as a dictionary."""
+        return {
+            "maxiter": self.maxiter,
+            "tol": self.tol,
+            "lr": self.lr,
+            "beta_1": self.beta_1,
+            "beta_2": self.beta_2,
+            "eps": self.eps,
+            "noise_factor": self.noise_factor,
+            "callback": self.callback,
+            "snapshot_dir": self.snapshot_dir,
+        }
+
+    def minimize(
+        self,
+        fun: Callable[[POINT], float],
+        x0: POINT,
+        jac: Callable[[POINT], POINT] | None = None,
+        bounds: list[tuple[float, float]] | None = None,
+    ) -> OptimizerResult:
+        """Minimize the scalar function using NAdam."""
+
+        result = OptimizerResult()
+        x = np.array(x0, dtype=float)
+        self._m = np.zeros_like(x)
+        self._v = np.zeros_like(x)
+        self._t = 0
+
+        for i in range(self.maxiter):
+            self._t += 1
+
+            # Compute gradient numerically
+            grad = self.gradient_num_diff(x, fun, self.eps)
+
+            # Add optional stochastic noise
+            grad += self.noise_factor * np.random.randn(*grad.shape)
+
+            # NAdam update rule
+            m_hat = self.beta_1 * self._m + (1 - self.beta_1) * grad
+            v_hat = self.beta_2 * self._v + (1 - self.beta_2) * (grad ** 2)
+
+            m_corr = m_hat / (1 - self.beta_1 ** self._t)
+            v_corr = v_hat / (1 - self.beta_2 ** self._t)
+
+            # Nesterov momentum
+            x_update = self.lr * (self.beta_1 * m_corr + (1 - self.beta_1) * grad / (1 - self.beta_1 ** self._t)) / (np.sqrt(v_corr) + self.eps)
+            x -= x_update
+
+            # Update state
+            self._m = m_hat
+            self._v = v_hat
+
+            fval = fun(x)
+
+            # Callback
+            if self.callback is not None:
+                self.callback(self._t, x, fval)
+
+            # Save snapshot
+            if self.snapshot_dir is not None:
+                np.save(os.path.join(self.snapshot_dir, f"nadam_m_{i}.npy"), self._m)
+                np.save(os.path.join(self.snapshot_dir, f"nadam_v_{i}.npy"), self._v)
+                np.save(os.path.join(self.snapshot_dir, f"nadam_x_{i}.npy"), x)
+
+            # Check convergence
+            if np.linalg.norm(x_update) < self.tol:
+                break
+
+        result.x = x
+        result.fun = fun(x)
+        result.nfev = self._t
+        result.nit = self._t
+
+        return result
+
+    def load_params(self, snapshot_dir: str):
+        """Load optimizer state from snapshot files."""
+        last_iter = max([int(f.split("_")[-1].split(".")[0])
+                        for f in os.listdir(snapshot_dir) if f.startswith("nadam_x_")], default=-1)
+        if last_iter >= 0:
+            self._m = np.load(os.path.join(snapshot_dir, f"nadam_m_{last_iter}.npy"))
+            self._v = np.load(os.path.join(snapshot_dir, f"nadam_v_{last_iter}.npy"))
+            # x can also be restored if needed
+            # x = np.load(os.path.join(snapshot_dir, f"nadam_x_{last_iter}.npy"))
+            self._t = last_iter + 1
diff --git a/test/optimizers/test_nadam.py b/test/optimizers/test_nadam.py
new file mode 100644
index 000000000..03a7cdf55
--- /dev/null
+++ b/test/optimizers/test_nadam.py
@@ -0,0 +1,96 @@
+# This code is part of a Qiskit project.
+#
+# (C) Copyright IBM 2019, 2025.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Test of NAdam optimizer"""
+
+import unittest
+import tempfile
+import numpy as np
+from ddt import ddt
+
+# Import the test case base class
+from test.algorithms_test_case import QiskitAlgorithmsTestCase
+from qiskit_machine_learning.optimizers import NAdam
+from qiskit_machine_learning.utils import algorithm_globals
+
+
+@ddt
+class TestOptimizerNAdam(QiskitAlgorithmsTestCase):
+    """Test NAdam optimizer"""
+
+    def setUp(self):
+        super().setUp()
+        algorithm_globals.random_seed = 42
+        self.quadratic_objective = lambda x: x[0] ** 2 + x[1] ** 2
+        self.initial_point = np.array([1.0, 1.0])
+
+    def test_optimizer_minimize(self):
+        """Test NAdam optimizer minimize method"""
+        nadam = NAdam(maxiter=200, tol=1e-6, lr=1e-1)
+        result = nadam.minimize(self.quadratic_objective, self.initial_point)
+        self.assertAlmostEqual(result.fun, 0.0, places=6)
+        self.assertTrue(np.allclose(result.x, np.zeros_like(self.initial_point), atol=1e-2))
+
+    def test_optimizer_with_noise(self):
+        """Test NAdam optimizer with noise factor"""
+        nadam = NAdam(maxiter=150, tol=1e-6, lr=1e-1, noise_factor=1e-2)
+        result = nadam.minimize(self.quadratic_objective, self.initial_point)
+        self.assertAlmostEqual(result.fun, 0.0, places=4)
+        self.assertTrue(np.allclose(result.x, np.zeros_like(self.initial_point), atol=1e-2))
+
+    def test_save_load_params(self):
+        """Test save and load optimizer parameters"""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            nadam = NAdam(maxiter=100, tol=1e-6, lr=1e-1, snapshot_dir=tmpdir)
+            nadam.minimize(self.quadratic_objective, self.initial_point)
+            new_nadam = NAdam(snapshot_dir=tmpdir)
+            new_nadam.load_params(tmpdir)
+
+            self.assertTrue(np.allclose(nadam._m, new_nadam._m))
+            self.assertTrue(np.allclose(nadam._v, new_nadam._v))
+            self.assertEqual(nadam._t, new_nadam._t)
+
+    def test_settings(self):
+        """Test settings property"""
+        nadam = NAdam(maxiter=100, tol=1e-6, lr=1e-1)
+        settings = nadam.settings
+        self.assertEqual(settings["maxiter"], 100)
+        self.assertEqual(settings["tol"], 1e-6)
+        self.assertEqual(settings["lr"], 1e-1)
+        self.assertEqual(settings["beta_1"], 0.9)
+        self.assertEqual(settings["beta_2"], 0.999)
+        self.assertEqual(settings["eps"], 1e-8)
+        self.assertEqual(settings["noise_factor"], 1e-8)
+        # NAdam does not have amsgrad, so just check key safely
+        self.assertIsNone(settings.get("amsgrad"))
+        self.assertEqual(settings["snapshot_dir"], None)
+
+    def test_callback(self):
+        """Test using the callback."""
+        history = {"ite": [], "weights": [], "fvals": []}
+
+        def callback(n_t, weight, fval):
+            history["ite"].append(n_t)
+            history["weights"].append(weight)
+            history["fvals"].append(fval)
+
+        nadam = NAdam(maxiter=100, tol=1e-6, lr=1e-1, callback=callback)
+        nadam.minimize(self.quadratic_objective, self.initial_point)
+
+        expected_types = [int, np.ndarray, float]
+        for i, (key, values) in enumerate(history.items()):
+            self.assertTrue(all(isinstance(value, expected_types[i]) for value in values))
+            self.assertEqual(len(history[key]), 100)
+
+
+if __name__ == "__main__":
+    unittest.main()