The evolution of smart factories within Industry 4.0 is fundamentally dependent on the seamless and intelligent movement of materials. Autonomous Material Handling Systems, particularly those employing advanced industrial robots, have transitioned from fixed automation to flexible, intelligent agents central to cyber-physical production systems. This article presents a comprehensive examination of the state of the art, challenges, and future directions in path planning and control algorithms for industrial robots deployed in material handling applications within smart manufacturing environments. Through a systematic literature review and analysis of emerging empirical research, it investigates the integration of real-time sensory data, the demands of dynamic and unstructured environments, and the necessity for robust, adaptive control strategies. The discourse highlights the critical gap between theoretical algorithmic advancements in controlled settings and their practical, reliable deployment in complex, real-world factory floors. It is argued that the next frontier lies in hybrid AI-driven approaches that synergize classical robotic control with machine learning, all while guaranteeing safety, efficiency, and interoperability within the Industrial Internet of Things ecosystem. This article concludes by proposing a multi-layered framework for next-generation autonomous material handling and outlines specific research trajectories to bridge existing gaps between simulation and reality.