This review explores the integration of machine learning (ML) with enterprise resource planning (ERP) systems, highlighting their impact on ERP optimization. ML algorithms, capable of extracting complex patterns from large datasets, enable ERP systems to make accurate predictions and data-driven decisions. These systems adapt dynamically based on real-time insights, enhancing efficiency and adaptability. AI solutions help make ML models within ERP systems more understandable for stakeholders, enabling real-time data processing and decision-making. The integration of IoT and ML with ERP is gaining significance, allowing for adaptable strategies supported by continuous learning and data-driven optimization. The review provides a comprehensive analysis of ML integration across various ERP applications, synthesizing recent research findings to offer insights into cutting-edge techniques and advancements in ML-driven ERP optimization. It also identifies challenges and future directions for ML in ERP, emphasizing the potential for intelligent, efficient, and innovative ERP systems. The review covers the background of ERP systems, their evolution, and the role of ML in enhancing their functionality. It discusses ML applications in data-driven decision-making, inventory management, production scheduling, quality control, and predictive maintenance. The integration of ML with ERP systems has led to significant improvements in operational efficiency, cost reduction, and decision-making. The review also highlights the importance of IIoT and ML in ERP systems, showcasing their potential to transform business processes and enhance competitiveness. The findings underscore the value of integrating IIoT and ML within the ERP framework, leading to more efficient and responsive enterprise systems. The review concludes that ML-driven ERP optimization holds great promise for enhancing productivity, adaptability, and competitiveness in the digital age.This review explores the integration of machine learning (ML) with enterprise resource planning (ERP) systems, highlighting their impact on ERP optimization. ML algorithms, capable of extracting complex patterns from large datasets, enable ERP systems to make accurate predictions and data-driven decisions. These systems adapt dynamically based on real-time insights, enhancing efficiency and adaptability. AI solutions help make ML models within ERP systems more understandable for stakeholders, enabling real-time data processing and decision-making. The integration of IoT and ML with ERP is gaining significance, allowing for adaptable strategies supported by continuous learning and data-driven optimization. The review provides a comprehensive analysis of ML integration across various ERP applications, synthesizing recent research findings to offer insights into cutting-edge techniques and advancements in ML-driven ERP optimization. It also identifies challenges and future directions for ML in ERP, emphasizing the potential for intelligent, efficient, and innovative ERP systems. The review covers the background of ERP systems, their evolution, and the role of ML in enhancing their functionality. It discusses ML applications in data-driven decision-making, inventory management, production scheduling, quality control, and predictive maintenance. The integration of ML with ERP systems has led to significant improvements in operational efficiency, cost reduction, and decision-making. The review also highlights the importance of IIoT and ML in ERP systems, showcasing their potential to transform business processes and enhance competitiveness. The findings underscore the value of integrating IIoT and ML within the ERP framework, leading to more efficient and responsive enterprise systems. The review concludes that ML-driven ERP optimization holds great promise for enhancing productivity, adaptability, and competitiveness in the digital age.