This research paper tries to find how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing credit scoring systems, traditionally led by models like FICO and Vantage Score. Through a blend of theoretical exploration and empirical analysis, the study examines the growing role of AI and ML in evaluating credit risk. It highlights the advantages of AI-powered models, including higher predictive accuracy, improved financial inclusion, and the potential to minimize human biases. Using case studies and real-world performance data, the research compares conventional models with AI-driven alternatives, particularly in terms of their effectiveness in credit decision-making and broadening access to credit. A mixed-method approach is adopted, combining qualitative insights from existing literature with quantitative case data. The study finds that AI systems can assess creditworthiness using alternative data sources—such as utility bills, social media behavior, and payment patterns—benefiting those with limited credit history. However, the widespread use of AI brings challenges, notably concerns about algorithmic bias, opacity in decision-making, data privacy, and regulatory compliance. It concludes by calling for ethical integration of AI in financial services, backed by regulatory updates and continued research to ensure fairness, accountability, and long-term sustainability.