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In modern manufacturing industries, unexpected equipment failures lead to increased downtime, maintenance costs, and reduced productivity. To address these challenges, this paper presents an integrated approach to predictive maintenance using Internet of Things (IoT) and Machine Learning (ML) techniques. The proposed system continuously monitors critical parameters such as temperature, voltage, and current of industrial equipment using embedded sensors. These sensor data are collected and processed by a microcontroller, which transmits real-time information to a cloud platform through an IoT module. The collected data are analyzed using machine learning algorithms to identify patterns and predict potential failures before they occur. An LCD display provides on-site monitoring, while a buzzer alerts operators during abnormal conditions. The system enables remote monitoring, data logging, and intelligent decision-making, thereby reducing unplanned downtime and improving operational efficiency. This integrated solution enhances reliability, optimizes maintenance schedules, and minimizes operational costs in manufacturing environments. The proposed model demonstrates a scalable and cost-effective framework for smart industrial maintenance aligned with Industry 4.0 standards.