The paper provides an overview of the current status and trends in silicon solar cell manufacturing, focusing on the value chain from feedstock production to ingot processing and solar cell fabrication. It highlights the importance of silicon-based solar cells in the commercial market, driven by technological advancements, cost reductions, and increased awareness of renewable energy. The authors discuss the main manufacturing techniques for silicon ingots, particularly the Czochralski and directional solidification methods, and their key characteristics. They address the major challenges in silicon ingot production, such as optimizing yield, reducing costs, and improving efficiency. The paper also reviews recent developments in solar cell technology, including the transition from Al-BSF to PERC and the emergence of TOPCon and SHJ cells. It emphasizes the role of artificial intelligence (AI) and machine learning (ML) in solving current PV industry challenges, such as analyzing structure loss in silicon ingots, optimizing solar cell design, and advanced defect characterization. The authors conclude by discussing the importance of recycling and sustainability in the PV sector and the potential of AI to drive operational improvements in the industry.The paper provides an overview of the current status and trends in silicon solar cell manufacturing, focusing on the value chain from feedstock production to ingot processing and solar cell fabrication. It highlights the importance of silicon-based solar cells in the commercial market, driven by technological advancements, cost reductions, and increased awareness of renewable energy. The authors discuss the main manufacturing techniques for silicon ingots, particularly the Czochralski and directional solidification methods, and their key characteristics. They address the major challenges in silicon ingot production, such as optimizing yield, reducing costs, and improving efficiency. The paper also reviews recent developments in solar cell technology, including the transition from Al-BSF to PERC and the emergence of TOPCon and SHJ cells. It emphasizes the role of artificial intelligence (AI) and machine learning (ML) in solving current PV industry challenges, such as analyzing structure loss in silicon ingots, optimizing solar cell design, and advanced defect characterization. The authors conclude by discussing the importance of recycling and sustainability in the PV sector and the potential of AI to drive operational improvements in the industry.