This research explores the use of machine learning (ML) to optimize bamboo treatment and seasoning for reinforced cement concrete (RCC) compound wall panels. The study focuses on improving key processes such as drying, splitting, and bending of bamboo, along with its treatment for waterproofing, anti-termite protection, and enhanced bonding with concrete. By analyzing environmental data and monitoring in real-time, ML models help to fine-tune these processes, ensuring consistent quality and durability of bamboo reinforcement. Predictive modeling enables the adjustment of treatment conditions and parameters, reducing waste and improving energy efficiency. Machine learning can enhance the effectiveness of bamboo processing, leading to stronger and more reliable RCC panels. This approach not only supports sustainable construction practices but also improves the performance and longevity of bamboo-reinforced concrete structures.