Autonomous marine navigation is a rapidly growing field with significant implications for maritime safety, logistics efficiency, and environmental monitoring. Traditional navigation systems rely heavily on human operators and predefined routes, which can be inadequate in dynamic maritime environments. Artificial Intelligence (AI), particularly in the form of decision engines, offers the ability to process realtime data from various onboard and external sensors to support autonomous, context-aware navigation. This paper examines the foundational technologies behind AI-powered decision engines for autonomous ships, including sensor fusion, machine learning, computer vision, and path planning algorithms. It discusses applications such as collision avoidance, dynamic route optimization, and environmental hazard detection. Case studies from autonomous shipping projects and maritime technology providers illustrate practical implementations and performance benchmarks. Ethical and regulatory challenges, including liability, international maritime law, and cybersecurity, are analyzed. The paper also addresses technical issues such as realtime responsiveness, multi-agent interaction, and extreme weather adaptability. Looking forward, it explores future innovations such as digital twins, reinforcement learning-based navigators, and inter-vessel communication protocols. AI-powered decision engines are poised to revolutionize marine navigation by enhancing safety, efficiency, and sustainability in global maritime operations.