Abstract: As a critical component of industrial transmission systems, the reliability of gears directly affects equipment safety and economic performance. To enable early prediction and precise maintenance of gear wear failures, oil analysis-based condition monitoring technology has become an important approach for predictive maintenance. However, traditional methods suffer from low detection efficiency and limited capability for multidimensional parameter correlation analysis of oil samples. In recent years, multiparameter fusion methods that integrate machine learning and mathematical modeling have significantly improved the accuracy of wear condition diagnosis and remaining useful life (RUL) prediction, addressing the engineering limitations of conventional offline inspections (requiring equipment shutdown and disassembly) and single-dimensional monitoring approaches (such as costly vibration analysis). This paper reviews the development and evolution of gear wear monitoring technologies by combining industrial case studies and academic research. It focuses on data fusion, statistical model optimization, and machine learning-driven oil multiparameter analysis strategies. Finally, the main challenges (such as data dependency, sensor calibration, and model generalization) and future directions (including miniature MEMS sensors, digital twins, and small-sample learning) are discussed. The study provides theoretical support and practical references for intelligent equipment health management.