The development of renewable and eco-friendly bio-lubricants can address the environmental challenges posed by petroleum-based lubricants. At the same time, it is possible to improve the tribological properties of lubricants through alternative sources. To overcome these problems, castor oil is a potential basis for bio-lubricants due to its high viscosity, natural lubricity, and biodegradability. In the current work, castor oil was chemically modified by the epoxidation process. This process has improved the tribological properties of castor oil through the epoxidation method. In this method, the presence of hydrogen peroxide acts as an oxidizing agent while sulfuric acid serves as a catalyst, converting the unsaturated double bonds present in the oil into oxirane rings. At the same time, this modification enhanced the thermal stability and tribological applications in harsh operating conditions. The tribological performance of the epoxidized castor oil, further reinforced with copper oxide (CuO) nanoparticles, was examined using a four-ball tribometer. In the present test, pressure, temperature, nanoparticle concentration, and rotational speed were considered as process parameters, while the Coefficient of Friction (COF) and Wear Scar Diameter (WSD) were evaluated as the primary responses. To understand the influence of these parameters and their interactions, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models were employed. The RSM model, developed to optimize the COF, showed a high correlation with R2 = 0.999, which was found to be highly consistent with the experimental results. This model provides more than 90% prediction accuracy and also highlights important interactions between various factors. The optimal operating conditions 0.2 wt.% CuO nanoparticles, 1300 rpm, 25 mbar, and 120 °C resulted in a minimum COF of 0.0409 and a WSD of 0.6288 mm. An ANN model trained using the Levenberg–Marquardt algorithm, configured with double hidden layers of ten neurons each, and further validated the RSM results. The network achieved prediction errors of less than 1% for both responses. Overall, the study concludes that CuO-enhanced epoxidized castor oil significantly improves friction and wear characteristics, representing a viable and sustainable alternative to mineral-based lubricants. The combined use of RSM and ANN provides a reliable pathway for optimizing future bio-lubricant formulations.