The need for optimized design, synthesis, and processing conditions is ubiquitous in materials science and technology, impacting fields as diverse as solar energy conversion, electronics, medicine, metallurgy, and energy storage. However, obtaining a target material with desired properties is time-intensive because of the high-dimensional and complex synthetic and processing parameters space. The sampling of the large synthetic and processing landscape is generally done through human intuition, based on the knowledge of physical chemistry principles, and trial-and-error approaches, leading to clustered, sparse and incomplete datasets. In the past decade, automated robotic technologies have been shown to markedly increase productivity in medicine, synthetic biology, chemistry/physics and materials science research fields by offloading repetitive works from human scientists and performing experiments at a faster speed, with greater precision, and better accuracy. With the aid of high-performance computing in recent years, artificial intelligence (AI) has been successfully applied to inorganic materials and small molecule discovery and engineering. Physics-informed machine learning approaches applied to clustering, regression, and Bayesian methods, as well as artificial neural networks, have emerged, and have used and contributed to publicly accessible databases producing new insights. This new integrated experimental and computational paradigm has enormous potential benefits, as the robotic hardware and AI software technologies required to realize this goal are just reaching maturity. The proposed symposium will address the main progress and challenges in the research of AI-guided sustainable (e.g., energy conversion and storage, climate, plastic upcycling) and electronic materials synthesis and processing, and cover the entire life cycle of these studies, from computational design, physically-guided AI, experimental automation, to control methods, synthetic databases, and robotic integration.