Application of optimization tools and techniques is necessary and an essential requirement for any metal cutting-based manufacturing unit to respond effectively to severe competitiveness and increasing demand of quality product in the global market. However, both problem types and techniques employed are diverse. Often the context of the problem involves building nonlinear inferential response surface model(s) of the process(s), and then determine levels of inputs and in-process parameters that result in best (or significantly improved than existing) measures of process quality improvement and effectiveness. Selecting the appropriate levels or settings of inputs and in-process variables is a typical example of desired process effectiveness. However, determination of optimal process conditions, using appropriate solution methodology through cost-effective inferential nonlinear response surface model(s) is a challenging and continual research endeavour for researchers and practitioners. In this context, artificial neural network (ANN) and metaheuristic strategies, such as genetic algorithm (GA), simulated annealing (SA), and tabu search (TS), either in its original form or its variant, has been shown to yield promising outcomes for solving nonlinear response surface optimization problems in metal cutting process(s). The goal of this chapter is to assess the status and scope of artificial neural network-based inferential model, GA, SA, and TS-based metaheuristic search stategies in metal cutting processes. Subsequently, a solution methodology for nonlinear response surface optimization in metal cutting processes is proposed for the benefits of selection of an appropriate technique. Specific application in a multiple response grinding process optimization problem using ANN, realvalued genetic algorithm, simulated annealing, and a modified tabu search is also provided for a clearer understanding of the settings, where the proposed methodology is being used.