In today’s interconnected world, mobile network performance is crucial for both personal and professional communication. Despite technological advancements, users often experience inconsistent network performance, particularly in areas with challenging geographical and environmental conditions such as varying terrain slopes, urban density, and natural obstacles. This variability frequently leads to poor call quality and increased call drop rates, diminishing user experience. The primary challenge is to analyze how various factors—including terrain slope, indoor versus outdoor usage, network type (e.g., 4G), and geographical location—affect mobile network performance. The dataset under examination includes 10,454 records with certain attributes, Additional variables like network operator, state, and seasonal changes further complicate the analysis. This project aims to develop a classification model to predict the call drop category based on the dataset’s attributes. By leveraging Business Intelligence, the goal is to identify patterns and correlations that can inform strategies for optimizing network performance. This will support strategic decision-making for network expansions and upgrades by pinpointing areas with poor performance—potentially due to terrain and environmental factors—and prioritizing them for improvement. Ultimately, this analysis will provide actionable insights to enhance user satisfaction and optimize resource allocation, ensuring efficient and effective investments in network infrastructure.