The convergence of artificial intelligence (AI) and additive manufacturing (AM) has led to transformative advancements in manufacturing, yet existing research remains fragmented, often focusing on isolated applications. A comprehensive understanding of AI’s role across the entire AM workflow remains limited, creating a gap in the literature. This study aims to bridge this gap by conducting a systematic review of AI applications throughout the digital-to-physical AM process chain, including design optimization, manufacturing program generation, support structure generation, process monitoring, post-processing, and machine maintenance. The research follows a systematic literature review methodology to analyze 213 selected studies from the Scopus database. The findings reveal that AI-driven approaches, particularly machine learning, deep learning, and reinforcement learning, enhance additive manufacturing efficiency, defect detection, and predictive maintenance, leading to improved process automation and quality control. This study contributes to the field by providing a holistic synthesis of AI applications in AM, identifying key challenges, and outlining future research directions.