Modern agricultural machinery, particularly smart tractors, represents a significant capital investment whose operational reliability directly impacts farm productivity and economic sustainability. Traditional reactive and time-based preventive maintenance strategies often result in unexpected equipment failures, excessive downtime, and suboptimal resource allocation in agricultural operations. Vibration-based condition monitoring has emerged as a powerful predictive maintenance approach that enables early detection of mechanical degradation in critical tractor components including engines, transmissions, hydraulic systems, and power take-off mechanisms. This article examines the integration of vibration sensors, data acquisition systems, and intelligent diagnostic algorithms for predictive maintenance of smart tractors and connected agricultural machinery. Key technologies discussed include accelerometer-based monitoring systems, wireless sensor networks, edge computing architectures, and machine learning algorithms for fault detection and classification. The implementation of vibration-based predictive maintenance systems has demonstrated significant improvements in equipment reliability, with reported reductions in unplanned downtime of 30-50% and maintenance cost savings of 20-40% compared to conventional approaches. Advanced signal processing techniques including time-frequency analysis, statistical feature extraction, and deep learning models enable accurate diagnosis of bearing defects, gear wear, misalignment, and imbalance conditions. The convergence of Internet of Things technologies, cloud computing, and artificial intelligence is transforming agricultural machinery maintenance from reactive approaches toward proactive, data-driven strategies that enhance operational efficiency and support sustainable intensification of agricultural production systems.