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The omnipresence of batteries in nowadays appliances such as portable devices, electrical vehicles, hybrid microgrids, etc. gives them a special priority in energy storage. In addition, the high cost of battery manufacturing makes the maintenance and the monitoring of their states critical. The state estimation is a complicated process that may include many parameters to take into consideration. The effective solutions can lead towards an efficient maintenance of the intended battery packs and can render a longer life of cells in that pack. This chapter focuses on the critical process of battery state estimation and the role of artificial intelligence (AI) techniques in enhancing this process. It highlights the importance of accurately assessing the current and future state of batteries, such as their capacity, voltage, and overall health. The integration of AI-driven battery state estimation in Battery Management Systems (BMS) is discussed, emphasizing the benefits it provides in terms of safety, performance, and energy management. The chapter also explores various rechargeable battery technologies and their applications in different industries. Furthermore, the authors considered the state-of-the-art approaches of the State-of-Charge (SoC), State-of-Health (SoH), Capacity, and Remaining Useful Life (RUL) estimation with a critical analysis of benefits versus challenges. it summarizes the key steps and techniques involved in AI-assisted battery state estimation, highlighting the advantages and limitations of using AI in this context.