This review article examines the integration of Artificial Intelligence (AI) in Sequential Batch Reactors (SBRs) to enhance operational efficiency in wastewater treatment. SBRs facilitate the treatment process through distinct phases—fill, react, settle, and decant—within a single tank. The transitions between these phases are pivotal for optimizing treatment performance. Traditional control strategies often rely on manual interventions and fixed schedules, which may lead to inefficiencies and suboptimal outcomes. AI technologies, including machine learning and predictive analytics, present a transformative opportunity to improve operational management by enabling real-time monitoring, adaptive control, and predictive maintenance. This article discusses various AI applications in SBR systems, highlighting their potential to enhance treatment efficiency, reduce energy use, and improve effluent quality. Furthermore, it addresses the challenges and future directions of implementing AI within wastewater treatment frameworks, emphasizing the need for continued innovation to meet evolving environmental standards. Through a comprehensive analysis, this review underscores the role of AI in revolutionizing wastewater treatment processes, paving the way for more sustainable and efficient practices in the management of water resources.