The growing complexity of the manufacturing processes has increased the need to use robust monitoring and maintenance strategies. Remote Process Monitoring Systems (RPMS) are IoT-based, artificial intelligence (AI), and predictive analytics systems that are redefining the operational landscapes through proactive interventions that reduce downtime, increase efficiency, and promote operational excellence. This paper is a critical analysis of the secondary sources, such as peer-reviewed journal articles, industrial reports, and case studies, as to how RPMS has contributed to the progress of predictive maintenance and manufacturing performance enhancement. The evidence shows that RPMS provides real-time visibility, alleviates expensive disruptions, and streamlines decision-making with data-driven knowledge. The thematic analysis reveals that there are some common trends across the literature, such as an increasing trend in using predictive maintenance, the operational benefits of optimizing the process, and specific sectoral uses in the automotive, energy, and food-processing sectors. Also, potential improvements, including the introduction of digital twins, advanced AI algorithms, and environmentally friendly approaches to monitoring, are discussed. The paper ends with suggestions for future research and practical implementation of RPMS.