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Lithium-ion batteries are extensively used in electric vehicles, renewable energy systems, and portable electronic devices due to their high energy density and efficiency. However, their performance and overall health degrade over time as a result of factors such as temperature fluctuations, humidity exposure, repeated charging–discharging cycles, and varying load conditions. Traditional battery monitoring techniques are primarily threshold-based, which often leads to inaccurate predictions under dynamic and real-world operating environments. This project presents an IoT-based intelligent battery condition prediction system utilizing an ESP8266 microcontroller integrated with a Support Vector Machine (SVM) machine learning algorithm. Various sensors, including voltage, current, temperature, and humidity sensors, are employed to continuously monitor key battery parameters. The collected data is transmitted and stored using IoT technology and further processed using Python-based machine learning models. The trained SVM model effectively classifies battery conditions, enabling efficient battery health and performance monitoring. This approach enhances prediction accuracy, thereby improving the safety, reliability, and lifespan of lithium-ion batteries. The system can also provide early warnings for potential battery failures, reducing maintenance costs and preventing unexpected breakdowns. Additionally, the integration of cloud-based storage allows remote access and analysis of battery data in real time. The proposed system is scalable and can be adapted for large-scale industrial and automotive applications. Overall, it supports smarter energy management and contributes to sustainable and efficient battery utilization.