This research examines Artificial Intelligence (AI) and Machine Learning (ML) applications in financial risk assessment and investment strategies for mergers and acquisitions (M&A). A comprehensive analysis of over 20,000 global deals from 2013-2023 shows that integrating Ai to predict M&As could reduce transaction failure rates and increase valuation accuracy. After testing multiple algorithms, the XGBoost classifier performed best for merger success with a recall of 60% and precision of 62% on historical data from the Institute of Mergers, Acquisitions and Alliances. The main contribution of this work is a new integrated framework that combines AI-driven quantitative analysis with human expert judgment, addressing the fundamental limitations of both purely algorithmic and solely human-driven approaches to M&A evaluation. This outperforms existing benchmarks in the literature and is a big step forward in predictability. The results also suggest explainable AI architectures, human-machine collaboration protocols and multidisciplinary training for professionals to ensure accountability, transparency and operational efficiency. The research concludes that AI in M&A is not just about the technology but about how it’s integrated with human judgment, ethical governance and context specific communication frameworks