<p>Frequent handovers, signaling overhead, latency, and packet loss all make it difficult to manage mobility effectively in 5G networks. These factors together compromise resource allocation, accuracy of changeover decisions, and network reliability. By integrating deep learning and meta-heuristic optimization, mobility prediction and handover performance can be improved significantly in a dynamic 5G environment. To validate this theory, this research proposes an integrated framework using Crackwave Residual Convolutional Neural Network (CRCNN) for mobility prediction and Enhanced Red Panda Optimization Algorithm (ERPOA) for optimally making handover decisions. By utilizing CRCNN, deep mobility features from users’ activity, handover requests, and real-time network variations, accurate mobility predictions are extracted and evaluated. Concurrently, by utilizing ERPOA, adaptive resource allocation helps maintain balance on cell load, reduce congestion, and minimize latency when making handover decisions through the utilization of the same computational factors used within CRCNNs. An implementation of this framework is executed in Matrix Laboratory (MATLAB) and compared against the benchmark methodologies. The results indicate that this proposed framework consistently outperformed competing methods, achieving an overall handover success rate of 98%. The framework has been proven to be effective and adaptable according to these results, and furthermore, it has improved connectivity, spectrum efficiency, and user experience.</p>