This study investigates the effect of silane‐treated jute/kenaf fibers on the flexural and hardness properties of polymer composites for potential biomedical applications, such as prosthetics and bio‐implants. Using response surface methodology (RSM) and artificial neural network (ANN), the optimal combination of nanoparticle percentage, silane concentration, and silane dipping duration was identified to enhance the mechanical properties of the composites. RSM, coupled with analysis of variance (ANOVA), evaluated the influence of these variables on composite performance, revealing that silane treatment significantly improved flexural strength, while all fiber‐related variables impacted both flexural strength and hardness. The silane dipping duration emerged as the most influential factor, with nanoparticle addition enhancing fiber–matrix interactions and promoting better agglomeration. The ANN model accurately predicted the composite's properties, with results strongly correlating to experimental data. The optimized formulation, consisting of 5% nanoparticle content, 10% silane treatment, and a 20‐min silane dipping duration, demonstrated a 26.22% increase in flexural strength and a 33.15% improvement in hardness. This optimized composite formulation holds promise for use in biomedical applications requiring high mechanical strength and durability, such as in prosthetic materials and orthopedic implants, where enhanced flexural strength and hardness are critical for longevity and performance.

This study investigates the mechanical properties of hybrid composites reinforced with jute, kenaf, and glass fibers, incorporating Aluminum Oxide (Al₂O₃) as a nanoparticle filler. The effects of three key parameters—fiber orientation, fiber sequence, and weight percentage of Al₂O₃ on—the tensile and impact strength of the composites were examined. Three levels for each factor were considered: fiber orientation (0°, 45°, and 90°), fiber sequence (1, 2, and 3 layers), and varying Al₂O₃ content (3%, 4%, and 5%). The response surface methodology (RSM) was employed to optimize the parameters, providing insights into the interactions between these factors and their influence on the composite's mechanical performance. Additionally, artificial neural networks (ANN) were used for prediction modeling. The outcome presented that the ANN model outpaced RSM in terms of accuracy, with a higher correlation between predicted and experimental values. The optimal parameters for achieving the highest tensile and impact strength were determined, with fiber orientation at 90°, fiber sequence at 3, and Al₂O₃ content at 5%. This study demonstrates the effectiveness of ANN in predicting the mechanical properties of the laminated composite and highlights the significant role of fiber orientation, sequence, and nanoparticle reinforcement in enhancing composite performance.