The rapid evolution of artificial intelligence (AI) has revolutionized retail banking, shifting service delivery from traditional standardized models to intelligent, hyper-personalized experiences. AI-driven solutions powered by machine learning (ML), natural language processing (NLP), and predictive analytics are redefining customer engagement, satisfaction, and operational efficiency. This research aims to assess the impact of AI-driven personalization on banking performance and customer retention while addressing challenges related to AI governance and ethical considerations. The research employs qualitative research methods, provides a historical analysis of market trends, presents practical examples, and offers forecasts to assess the role of AI in enhancing customer interactions, generating recommendations for financial products, and detecting fraud. Additionally, it examines the pace of AI adoption, analyzes industry statistics, and evaluates the regulatory framework shaping AI-driven banking services. In addition to advantages, this research also examines key challenges associated with AI-driven service personalization in banking and finds out how global regulations impose strict AI governance measures. As a result, financial institutions must prioritize explainable AI (XAI) frameworks, ethical personalization strategies, and bias mitigation techniques. By analyzing these factors and balancing the benefits of AI adoption with the need to minimize associated risks in retail banking, this research proposes a comprehensive roadmap for the transparent, trustworthy, and regulatory-compliant implementation of AI. This roadmap aims to ensure fairness, customer-centricity, and alignment with ethical and legal standards.