<p>Experiments involving natural fibers such as rattan and jute have gained significant attention due to the growing demand for sustainable Fiber-Reinforced Polymer (FRP) composites. The natural fibers attract researchers because their strength-to-weight ratio, low cost and sustainable nature provide substantial benefits to composite materials used in automotive and sporting equipment manufacturing. Researchers have identified two major issues with natural fiber which include its tendency to absorb moisture and its inability to maintain consistent quality, including proper matrix binding. This research aims to improve NFRCs through hybrid fiber usage and specific bonding treatment methods by enhancing sustainable materials through better mechanical performance and improved overall material performance. This research examined five key machining parameters, which included spindle speed, feed rate, depth of cut, cutting tool material and cutting force to determine their effects on sisal-rattan fiber-reinforced composites delamination behavior and surface roughness. Both simulation using response surface methodology and experimental measurements on machined composite samples are used to validate the research. The prediction of the optimal parameters is performed by using Quantum-Enhanced Maxout Convolutional Network (QEMCN) model, and the Eco-Gain Crested Porcupine Optimizer (EGCPO) utilized to enhance prediction accuracy. The results indicate that the average surface roughness is decreased by 15%, and 12% of the delamination factor in comparison with conventional rule-based optimization. Outcomes clearly indicate that this approach for process optimization is highly effective for sustainable FRP composites, offering significant potential to enhance real-world composite optimization processes in manufacturing.</p>