The efficacy of a machining system relies on its capability to meet various necessities simultaneously, viz., competent fault identification, detailed remaining life evaluation, higher product quality, and lower manufacturing expenses.  An efficient machining system, therefore, intensely relies upon the performance of diagnostics, prognostics, and process control policies on the shop floor. Although these functions have traditionally been regarded as highly interrelated, the available literature has mostly treated them separately. This paper reviews those functions comprehensively, investigating an emerging area of research in real-time integration. The review identifies critical gaps in the current research, such as the limitations of monitoring systems in dynamic environments, limited research on the relationship between tool degradation and product quality, and the absence of predictive models that consider future operating profiles. It further points out the non-uniformity of approaches toward tool health assessment and the relative scarcity of dynamic process quality control strategies. This research underscores the potential of integrating diagnostics, prognostics, and process control to advance intelligent and sustainable machining systems.