Machine learning (ML) projects often involve numerous experiments that need to be tracked, compared, and reproduced to ensure consistent results and effective collaboration. This paper explores the significance of experiment tracking in ML workflows, discusses best practices, and addresses challenges in implementation. We present a comprehensive framework for experiment tracking that enhances reproducibility, accountability, and collaboration within ML teams. This paper emphasizes on how systematic tracking can optimize workflows, accelerate model development, and improve the overall quality of machine learning projects.