Machining processes are one of the very essential processes in manufacturing systems. The processes are not only associated with material removal, but also associated with the finishing of the surface where the shape and size of the object does not change. The chapter covers mainly the experimental and analytical methods and solutions solving the problems of grinding wheel dressing, cladding morphology prediction, material removal rate associated with the spherical error of the lapping process, optimal warehouse distribution plan, high-speed laser surface hardening process. In addition, the complex different finite element approaches for temperature distribution of laser surface hardening have also been discussed. Moreover, several machine learning models such as regression analysis, artificial neural networks, support vector machines, gaussian regression models employed to the grinding machine parameter optimization are reviewed along with certain test cases and real-world case studies. The testing of applicability of the soft computing methods for optimization of rolling parameter prediction and subsequent schedule setting have also been elaborated. The linear programming, TOPSIS and fuzzy logic approaches addressing the consideration of manufacturability in early design stages are discussed with case studies. Also, the monarch butterfly algorithm and differential evolution approaches were discussed in accordance with the efficient facility/store layout.