Abstract: A system and method for generating optimized workshop schedules is disclosed, involving a multi-step process that integrates data collection, optimization, and machine learning. The method begins by collecting production data and routing information from an Enterprise Resource Planning (ERP) system or an Enterprise Data Warehouse (EDW) system. An optimization model then generates an initial workshop production schedule based on the collected data. This schedule is subsequently fine-tuned to create an enriched schedule that incorporates alternative routing information and more realistic data constraints. A supervised Machine Learning (ML) model is trained using the enriched schedule to learn the embedded data constraints and objectives. Finally, the trained ML model is deployed to generate highly accurate workshop schedules, ensuring efficient and effective production processes. This method enhances scheduling accuracy and operational efficiency in manufacturing environments.