Quantum computing (QC) is a paradigm shift and ushering in the discipline of computational power. It utilizes the unique principles of quantum–mechanical phenomena such as entanglement and superposition to solve problems that are impenetrable and beyond the reach of classical computers. For the field of mechanical and allied engineering, where complex simulations for a system, optimization of a system or process, and advanced material analyses are involved, the implications of quantum computing are profound. This chapter gives the insight of quantum computing within the field of mechanical and allied engineering, focusing on the key areas like optimization of structure and engineering design, material science, machine learning in engineering, computational fluid dynamics (CFD), and manufacturing process and control. This chapter examines several prominent quantum algorithms like the Quantum Approximate Optimization Algorithm (QAOA), Grover’s Algorithm, the HHL (Harrow-Hassidim-Lloyd) Algorithm for Quantum Linear Solver, the Variational Quantum Eigensolver (VQE), and Quantum Machine Learning (QML) and discusses how each algorithm can address the specific challenge encountered in mechanical engineering. Alongside the paradigm shift, this chapter also considers the current hurdles faced by the quantum computing field; these are issues of noise, errors and error rates, and scalability. The chapter concludes by highlighting promising avenues and research gaps for quantum computing in the workflow of mechanical engineering. At the end of the chapter, this chapter aims to provide an overview of how quantum computing and its operation could reshape mechanical and allied engineering in the years to come.