Predictive maintenance is a critical strategy in today's business landscape, focusing on predicting equipment failures and other potential issues before they happen. It involves collecting and analyzing data from various sources such as sensors and historical records to optimize maintenance schedules, reduce unplanned downtime, and improve operational efficiency. Advanced analytical techniques like machine learning, artificial intelligence, and statistical modeling are used to process the collected data and generate estimates about the remaining useful life of equipment or the likelihood of failure within a specific time frame. Predictive maintenance plays a valuable role in managing assets and devices in IoT environments, where sensors continuously monitor data points like temperature, vibration, and pressure while transmitting that data for analysis. By shifting from reactive to proactive maintenance, organizations can achieve cost savings, increased asset longevity, enhanced productivity, and other benefits. Various tools are required to take advantage of predictive maintenance, including data collection tools, connectivity protocols, data storage solutions, data processing platforms, analytics and forecasting tools, and visualization libraries or frameworks. Some popular platforms for IoT and predictive maintenance include PTC ThingWorx, IBM Maximo, Azure IoT Hub, AWS IoT Core, and InfluxDB.