This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials Acceleration Platforms (MAPs) aim to reduce this to 1-2 years through closed-loop systems combining physical experimentation with computational intelligence. The review analyzes critical components including robotic hardware architectures, integrated analytical instrumentation, closed-loop optimization strategies, AI-driven decision-making frameworks, and multi-agent systems for laboratory coordination. Case studies demonstrate remarkable success across various domains: nanomaterials synthesis, inorganic materials exploration, electrocatalyst discovery, and reaction mechanism elucidation. Large language models have recently enhanced these systems by improving knowledge extraction, experimental planning, and multi-agent coordination. The integration of standardized data formats, accessible materials databases, and sophisticated knowledge representation frameworks further accelerates discovery while enhancing reproducibility. This transformative paradigm represents a fundamental reimagining of materials science methodology, shifting from human-guided exploration to AI-orchestrated discovery campaigns.