This comprehensive review evaluates the integration of machine learning (ML) with enterprise resource planning (ERP) systems, highlighting the impact of these trends on ERP optimization. The review covers recent advancements in ML integration within ERP environments, emphasizing the ability of ML algorithms to extract intricate patterns from large datasets, enabling more accurate predictions and data-driven decisions. ML enhances ERP systems' adaptability and efficiency by processing real-time data, leading to improved decision-making and resource allocation. The review also discusses the challenges and benefits of ML in ERP, including improved functionality, cost savings, and operational efficiency. It explores various applications of ML in ERP, such as inventory management, production scheduling, quality control, and predictive maintenance, and examines the integration of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) in ERP systems. The review provides a detailed analysis of state-of-the-art techniques and emerging trends, offering valuable insights for researchers, practitioners, and decision-makers. The integration of ML and IIoT in ERP systems is expected to drive further advancements in business technology, enhancing decision-making and operational efficiency in the digital age.This comprehensive review evaluates the integration of machine learning (ML) with enterprise resource planning (ERP) systems, highlighting the impact of these trends on ERP optimization. The review covers recent advancements in ML integration within ERP environments, emphasizing the ability of ML algorithms to extract intricate patterns from large datasets, enabling more accurate predictions and data-driven decisions. ML enhances ERP systems' adaptability and efficiency by processing real-time data, leading to improved decision-making and resource allocation. The review also discusses the challenges and benefits of ML in ERP, including improved functionality, cost savings, and operational efficiency. It explores various applications of ML in ERP, such as inventory management, production scheduling, quality control, and predictive maintenance, and examines the integration of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) in ERP systems. The review provides a detailed analysis of state-of-the-art techniques and emerging trends, offering valuable insights for researchers, practitioners, and decision-makers. The integration of ML and IIoT in ERP systems is expected to drive further advancements in business technology, enhancing decision-making and operational efficiency in the digital age.