Mergers and acquisitions are pivotal strategies employed by companies to maintain competitiveness, leading to enhanced production efficiency, scale, and market dominance. Due to their significant financial implications, predicting these operations has become a profitable area of study for both scholars and industry professionals. The accurate forecasting of mergers and acquisitions activities is a complex task, demanding advanced statistical tools and generating substantial returns for stakeholders and investors. Existing research in this field has proposed various methods encompassing econometric models, machine learning algorithms, and sentiment analysis. However, the effectiveness and accuracy of these approaches vary considerably, posing challenges for the development of robust and scalable models
