This research is focused on the integration of advanced machine learning methods with real- time vibration and acoustic signal data to develop a smart predictive maintenance (PdM) system that can detect failure-tendency components of industrial machinery before catastrophic failure. The prime objective is to reduce unplanned downtime, enhance operational efficiency, and enable cost-effective maintenance planning by pre-fault detection.The paper begins by analyzing how maintenance practice has transformed from traditional reactive and preventive approaches to modern condition-based and predictive maintenance systems. There is particular emphasis put on theshift towards data-driven approaches that leverage continuous sensor input for timely decision-making. With the capture of both vibration and acoustic patterns, the research emphasizes the relevance of multi-modal sensor fusionto enhance diagnostic performance.