This research focuses on the development and characterization of a jute fiber-reinforced polymer composite with the incorporation of titanium carbide (TiC) nanoparticles. The composite's mechanical properties and performance were evaluated through tensile, impact, and wear tests. Additionally, a machine learning model based on linear regression was developed to predict the responses of these tests. The experimental results revealed that higher percentages of TiC nanoparticles significantly improved the tensile strength and impact resistance of the composite. The inclusion of TiC resulted in enhanced mechanical properties, enabling the composite to withstand external forces and deformations more effectively. Furthermore, the wear test demonstrated that higher TiC compositions led to reduced wear rates and friction coefficients, indicating improved wear resistance and tribological performance of the composite. This highlights the potential of TiC nanoparticles as effective reinforcements for enhancing the durability and longevity of jute fiber-based composites. The developed linear regression model exhibited strong predictive capabilities, achieving high R-squared values and accurate alignment with experimental results. This model serves as a reliable tool for estimating the responses of the composite under different test conditions, enabling efficient material characterization and performance prediction. Overall, this research contributes to the understanding of the effects of TiC nanoparticles on the mechanical and tribological properties of jute fiber-reinforced polymer composites. The findings emphasize the potential of TiC as an additive to enhance the overall performance and applicability of these composites. The developed linear regression model provides a valuable tool for predicting and optimizing the composite's responses based on input variables, facilitating the design of durable and sustainable solutions in various industries