Background: Artificial intelligence integration in due diligence procedures of mega-large merger acquisition dealings has been brought out as a powerful method in the American business world. Conventional due diligence processes tend to have a weakness when handling large volumes of financial and operational information in an efficient manner. Advanced analytics and machine learning algorithms have proven to hold a lot of promise in terms of improving the accuracy of the evaluation of transactions, and also in terms of mitigating time pressures that are normally promulgated in view of intricate MandA assessments. The development of AI-based tools has provided ways where financial institutions and corporate organizations can make better strategic decisions by analysing all the data and predicting the outcomes. The AI technologies provide improved activities in risk assessment, identification of targets, and evaluation of transactions in various markets in the USA. The machine learning algorithms will make it possible to evaluate the financial results, operational synergies, and market positioning aspects that determine success rates of transactions with greater precision. Materials and Methods: The research used a sophisticated and rigorous methodology of secondary data analysis as it analyzed 215,160 cross-border M and A deals and concerned data in the Thomson Reuters SDC Platinum database over the 1973-2018 period. We have applied the machine learning algorithms based on AdaBoost and support vector machine models as a predictive analysis. The preprocessing of data involved feature extraction of ESG scores, financial indicators and the metrics of sustainable development at country-level. The study involved a total of 215,160 cross-border M and A deals in 58 states with special attention paid to those based in the USA. The data preprocessing involved feature extraction, principal component analysis and 10-fold cross-validation methods. Results: Analysis indicated that AI-based due diligence platforms have a prediction accuracy of 80.1% in the determination of merger success when compared to traditional approaches, which had 62.7% accuracy. ML algorithms are good at working with multi-dimensional datasets such as the ones involving ESG characteristics, financial indicators, and market intelligence factors. The application of natural language processing technologies cuts down time frames of contract analysis by 60-70% with no loss of regulatory compliance assessment. The frameworks of risk evaluation integrating AI algorithms outperform in identifying possible transaction hurdles and synergy in the various sectors of the industry. Discussion: The effectiveness of AI applications in due diligence efficiency and accuracy has been seen in significant markets of the USA. Machine learning algorithms are effective at processing multi-dimensional data such as environmental, social and governance factors and they minimize human bias in rating transactions. The technology can real-time risk evaluation and an improvement in decision making abilities of USA corporations under various geographical markets. The use of frameworks based on AI facilitates strategic decision making by eliminating human error and bias in making the complex transaction decisions. Conclusion: Machine learning programs can greatly improve the efficiency and precision of the due diligence process in large-scale merger and acquisition businesses by making it easier to check the analysis with greater detail and speed. Future development of AI technologies is likely to enhance further predictive modeling, risk evaluation and the process of strategic analysis in the international asset acquisitions and sales marketplace.