Machining processes are at the center of manufacturing processes. They govern the quality and functioning of the final product. The optimization of the parameters associated with the machining process is very essential. The chapter covers machining processes such as, milling, drilling, electrical discharge machining, abrasive waterjet machining, electrochemical machining and associated parameters such as material removal rate and electrode wear rate, multi-step production process, turning force, surface roughness and deviation of the depth of cut, micro-hardness, wear volume, cladding efficiency, input voltage, tool feed rate, several tolerances, etc. with focus on the importance and significance with a view point of optimization. The solution methodologies such as certain variants of cohort intelligence algorithm, ANN, moth search algorithm, particle swarm optimization, conditional design optimization, genetic algorithms, tabu search, simulated annealing, response surface methodology, design of experiment, grey relational analysis, TOPSIS, NSGA, ANOVA, lexicographic method, variations of teaching–learning-based optimization, Jaya algorithm, grey wolf optimizer, principal component analysis have been discussed for the single and multi-objective as well as multi-criteria optimization approaches. The chapter highlights the future directions of the applications of these methods for several traditional as well as non-traditional machining processes.