Machine learning (ML) has become a transformative technology across industries, significantly enhancing automation, decision-making, and predictive modeling. However, biases present in data can unintentionally be reinforced or even amplified by ML algorithms, leading to unfair and potentially harmful outcomes. This study presents a comprehensive framework to address bias identification and mitigation within ML data pipelines, ensuring fairness and accuracy. We explore strategies for detecting and correcting bias across different stages of the ML pipeline, including pre-processing, in-processing, and post-processing methods. Each stage offers distinct opportunities for intervention to minimize bias effectively. Case examples illustrate the practical application of these strategies in real-world scenarios, providing a tangible view of how bias mitigation can be implemented across diverse applications. Validation results on datasets with known bias issues demonstrate the framework’s ability to reduce bias without compromising model performance. This approach emphasizes the importance of proactive bias management within ML development, encouraging ethical and equitable model outcomes across various industries