The explosive growth of network complexity in 5G and beyond demands sophisticated handover techniques to ensure seamless user experience in dynamic environments. Traditional methods struggle with diverse service demands and volatile network conditions. Existing research largely focuses on individual ML techniques for handover. This chapter addresses this gap and proposes a comprehensive framework integrating various algorithms for enhanced performance. We address the limitations of traditional handovers by proposing the integration of Artificial Intelligence (AI) and Machine Learning (ML) for intelligent and efficient handover decision-making. We explore a range of ML techniques: Predictive Modeling for Supervised learning models that identify handovers based on network and user data; Time-Series Analysis to analyze historical data to uncover patterns and predict future handovers; Anomaly Detection to identify unusual network behavior to proactively trigger early handovers; and Reinforcement Learning to train agents to make optimal handover decisions in uncertain environments. The originality and novelty of the study is in introducing a Hybrid ML Approach that combines predictive models with real-time adaptation through reinforcement learning. Secondly, the contributions of the study include offering a comprehensive review of AI-ML-based handover techniques for next-gen wireless networks, proposing a novel hybrid ML framework for intelligent and efficient handover decisions and evaluating the performance of various algorithms through simulations and providing guidelines for practical implementation.