Small and Medium Enterprises (SMEs) play an important role in Malaysia’s economy. These SMEs are adopting new technologies like 5G that can accelerate their growth. However, 5G introduces ultra-dense network deployment that rely on small cells for faster communication and managing handover between these cells is a complex task, especially when user equipment (UE) moves from one cell to another. If the handover is not managed efficiently, it can lead to in frequent service interruptions. In 5G, handover refers to transferring active user session from one cell to another and this activity occur more frequently because of the denser network deployments. Therefore, smart handover techniques use AI-based algorithms that utilize historical data, learn from it, and dynamically adjust handover parameters based on real-time feedback rather than fixed or static thresholds. This chapter presents a framework that integrates Artificial Intelligence (AI) and Self-Organizing Network (SON) technologies to automatically manage handovers in 5G networks. The proposed approach focuses on optimizing key handover parameters, particularly Handover Margin (HOM) and Time-To-Trigger (TTT), using real-time network data and is designed to operate efficiently at the network edge through Multi-access Edge Computing (MEC). In addition to the framework, this chapter also explores handover management techniques and outlines current challenges and future research directions. The aim is to make 5G more practical and accessible for Malaysian SMEs by providing a smart, adaptive solution that improves network reliability and user experience.