This study explores the use of Artificial Neural Network and Particle Swarm Optimization to improve the performance and emissions of a CRDI engine fuelled by a mixture of co-pyrolyzed oil and diesel. Experimental results show that the mixed fuels perform better than pure diesel in various engine conditions, achieving higher brake thermal efficiency and lower smoke emissions. The blend D90P10 has the highest BTE of 37.4% working at FIT 30°bTDC and FIP 500 bar. However, NO and CO2 emissions were increased for the same condition. A multi-parametric ANN model is created to build a predictive model based on this experimental data, relating inputs (fuel injection pressure, fuel injection timing, and blend percentages) to outputs (Brake Specific Fuel Consumption, NO, smoke, CO2 emissions). The ANN model attains high accuracy with R2 values of 0.99, 0.98, 0.99, and 0.99 for BSFC, NO, Smoke, and CO2 emission predictions, respectively. The results indicate that for optimal performance, the best conditions are a FIT of 29.71ºbTDC, a FIP of 500 bar, and a co-pyrolysis oil blend percentage of 19.61%. On the other hand, to minimize engine emissions, the best conditions are FIT of 20.28ºbTDC, FIP of 300 bars, and a blend percentage of 25.6%. A balanced approach results in an optimal condition of FIT at 29.5ºbTDC, FIP of 500 bars, and a blend percentage of 6.11%. Experimental validation of these optimal predictions shows a maximum error of only 3.18%, highlighting the reliability and accuracy of the model.