The battery is the most crucial and costly component in electric cars, serving as the sole provider of electric power. However, the gradual weakening of the battery's power source leads to reduced performance, posing a significant concern for battery makers. This research proposes utilizing Internet of Things (IoT) methods to monitor and display battery performance. Various measurements such as voltage, current, and temperature are monitored, analyzed, and displayed to alert users about overcharging and other issues through diverse sensors. A microcontroller unit receives data on voltage, current, and temperature and sends battery data to the cloud for display. This paper details a monitoring framework for both battery-powered and fuel-type vehicles, tested under specific conditions to ensure battery state operations are effectively monitored. It also describes the different functions of a Battery Management System (BMS) with a focus on accurate state of charge (SoC) estimation. The advantages and drawbacks of various estimation methods are presented, highlighting how a BMS can utilize these indicators. Electric vehicles (EVs) are at the forefront of sustainable transportation solutions, with their widespread adoption dependent on battery pack performance and longevity. Monitoring and computing systems are essential for ensuring the reliability, efficiency, and safety of EV batteries throughout their operational lifespan. This paper provides an overview of current methodologies, technologies, and advancements in EV battery pack monitoring and computing. It examines the role of sensors, data acquisition techniques, and computational algorithms in assessing key parameters such as SoC, state of health (SOH), and remaining useful life (RUL) of battery packs. Additionally, it explores the integration of real-time data processing, machine learning models, and predictive analytics for proactive maintenance and performance optimization. Challenges such as data security, standardization, and scalability are discussed, alongside potential future directions to enhance the effectiveness of EV battery monitoring and computing systems. This review aims to contribute to the ongoing development and implementation of robust monitoring solutions essential for advancing the EV ecosystem.