Artificial Intelligence (AI) has emerged as a transformative force in modern healthcare, particularly in clinical decision-making processes. AI-powered systems can analyze vast quantities of medical data, identify patterns, support diagnoses, predict patient outcomes, and assist clinicians in developing personalized treatment plans. The integration of machine learning, deep learning, natural language processing, and predictive analytics into healthcare has the potential to improve diagnostic accuracy, enhance patient safety, optimize resource utilization, and reduce healthcare costs. Despite these opportunities, the implementation of AI in clinical settings raises several challenges, including data privacy concerns, algorithmic bias, explainability issues, regulatory barriers, and ethical dilemmas. This study examines the opportunities and challenges associated with AI-assisted clinical decision-making through a comprehensive review of existing literature and clinical applications. The findings indicate that while AI can significantly augment healthcare delivery, successful implementation requires robust governance frameworks, transparent algorithms, clinician training, and ethical oversight. The paper concludes that AI should function as a decision-support tool that complements rather than replaces human clinical expertise.