Modern industries have mechanical systems that must be monitored continuously and in realtime to guarantee the reliability of mechanical operations, minimize the downtime, and guarantee the improved safety. Other conventional maintenance methods such as reactive scheduled maintenance check are inadequate in complex industrial environment. This combination of smart sensors, advanced data analytics, and artificial intelligence offers the predictive maintenance capability, early fault detection, and intelligent decision making capability. This literature review presents a synthesis of the recent trends in the smart sensor technology like vibration, strain, temperature, MEMS, fibre optic and nanocarbon based sensors, and AI based data analysis methods like machine learning, deep learning, and soft sensor models. The review goes further to discuss techniques of combining sensors with cloudedge computing, Internet of Things platforms and digital twin frameworks to attain real time monitoring and automatic maintenance. The main issues such asthe inability to combine multisensors, low interpretability of AI models, energy inefficiency, the absence of unified frameworks, and interoperability problems are discussed. The research gaps and future directions in the study include explainable AI, sustainable low power sensor design, crossdomain validation, and scalable cyber physical architectures. The review is a good generalization of the state of capabilities, limitations, and opportunities that can be used in the design of intelligent, reliable, and sustainable mechanical monitoring systems by researchers and practitioners in accordance with Industry 4.0 and Industry 5.0 goals. Smart Sensors, Predictive Maintenance, Machine Learning, Real Time Monitoring, IoT, Digital Twin, Fault Detection, Industrial Automation. Smart sensors, Data analytics, Artificial intelligence, Real time monitoring, Predictive maintenance, Internet of Things, Digital twin, Mechanical systems, Fault detection, Industrial monitoring. Smart Sensors, Predictive Maintenance, Machine Learning, Real Time Monitoring, IoT, Digital Twin, Fault Detection, Industrial Automation. 63-74 Smart Innovations in Computer Science and Applications Special Issue Aman Padole