Operations research (OR) techniques have been widely used for optimizing problems, such as manufacturing scheduling, supply chain optimization, and resource allocation. Despite its effectiveness, traditional OR, especially exact methods, often struggle with scalability, computational efficiency, and adaptability to the dynamic and uncertain environments of Industry 4.0. While machine learning (ML) advancements provide novel approaches for addressing these challenges, they also present limitations, such as the lack of guaranteeing exact solutions and the need of relevant data. Therefore, the integration of OR and ML offers a balanced solution, leveraging ML's capability to extract patterns from large datasets and making predictive decisions and OR's precision to enhance decision-making processes, especially in scheduling tasks withing the context of Industry 4.0. This combination not only improves solution robustness and efficiency but also mitigates individual limitations of both fields. and make predictive decisions under uncertainty complements the decision-making process of OR. This paper aims to conduct a bibliometric analysis and a brief literature review on the integration of ML and OR, focusing on their application in scheduling problems.