The accurate estimation of state of charge and state of health of lithium-ion batteries is vital for the reliable operation and management of battery-powered systems, such as electric vehicles and renewable energy storage systems. This project introduces an innovative approach to battery state estimation by leveraging the multi-sensor fusion techniques. The fusion of multi-sensor data, processed through advanced machine learning models, allows for a more comprehensive and accurate assessment of the battery condition, enhancing prediction accuracy and reliability. This can contribute to the growing demand for reliable battery management systems.