<p>Electroless Nickel-Copper-Phosphorous coating was deposited onto Copper substrate for 30 minutes by varying the coating bath parameters such as the concentration of Nickel Sulphate, Copper sulphate and Sodium hypophosphite. This experimentation was conducted to develop the predictive models using Machine Learning approaches considering deposited mass per unit area and surface roughness as the responses. The Multi-linear regression and multivariate polynomial regression models have been used to develop the predictive models. The R² values of the multi-variate polynomial regression models were improved from the multi-linear regression models. Further, the predictive models explain that a trade-off between the coating deposited per unit area and surface roughness is needed, therefore, the models with higher accuracy were further analysed using Non-Dominant Sorting Genetic Algorithm-II to evaluate the optimum deposition condition which can maximize the coating deposition and reduce the surface roughness within the ranges of the process parameters. The Pareto optimal front of solutions obtained from Non-Dominant Sorting Genetic Algorithm II has given the optimal values of the process parameters which has provided additional choices to the decision maker. The coating was deposited in the optimal condition which converged with the results obtained from the optimization algorithm.</p>