The integration of artificial intelligence (AI) and robotics into chemical synthesis has given rise to autonomous laboratories, transformative systems designed to overcome limitations in traditional experimental approaches. This review synthesizes recent advancements in autonomous laboratory systems, highlighting their applications in chemical synthesis and the innovations driving their evolution. Autonomous laboratories combine automated hardware, intelligent software, and adaptive systems to optimize experimental workflows, reduce human intervention, and enhance efficiency in complex reaction environments. Key developments include AI-driven reaction pathway planning, closed-loop optimization frameworks, and robotic platforms capable of executing multi-step synthesis with minimal expert oversight. Leading research groups have demonstrated significant progress, such as AI-guided discovery of functional materials, automated photocatalytic reaction optimization, and self-learning microfluidic systems. This review provides a comprehensive analysis of current achievements and remaining gaps, offering insights for researchers and policymakers in advancing this transformative technology.