Autonomous vehicles (AVs) are transforming modern transportation systems through the integration of artificial intelligence (AI), machine learning, and advanced sensing technologies. These vehicles must continuously perceive dynamic environments, interpret complex traffic conditions, and make safe driving decisions in real time. However, relying on a single sensor is insufficient due to environmental uncertainties such as noise, occlusion, lighting variations, and adverse weather. To address these challenges, autonomous vehicles implement multi- sensor fusion techniques combined with intelligent decision- making models. This paper presents a detailed study of autonomous vehicle architecture, sensor technologies, sensor fusion strategies, AI-based decision-making models, real-world applications, challenges, and future research directions. The objective is to provide a comprehensive understanding of how AI and data science enable reliable and safe autonomous driving systems.