Cache placement in edge computing is crucial in optimizing data retrieval efficiency, particularly in environments characterized by fluctuating content demand and constrained storage capacity. This study proposes a machine learning-driven approach to enhance cache placement decisions, employing Logistic Regression and Support Vector Machines as predictive models. To refine classification accuracy and computational efficiency, the models are optimized through gradient-based techniques such as Stochastic Gradient Descent, Mini-Batch Gradient Descent, Momentum Gradient Descent, Adam, and Newton's Method. The methodology involves processing real-world access data, where key features such as response time, access frequency, and file size inform caching decisions. Experimental evaluations reveal that Newton's Method delivers the highest classification accuracy 97.5% with rapid convergence, while Momentum Gradient Descent achieves the highest cache hit rate 13% at a cache size of 150 MB. Furthermore, the proposed machine learning models significantly surpass traditional caching strategies such as Least Recently Used and Least Frequently Used, achieving up to a 100% improvement in cache hit rate. These findings underscore the effectiveness of adaptive, data-driven caching techniques in enhancing network performance and reducing latency in real-time edge computing environments.