Reproducibility in machine learning (ML) refers to the ability to replicate the results of a model when the same data and methods are used. This concept is crucial for validating research findings and ensuring that models perform consistently across different environments. The reproducibility crisis in ML has emerged due to challenges such as inadequate documentation, lack of standardized practices, and the complexity of ML frameworks. Addressing these issues is essential for building trust in ML technologies and fostering collaboration among researchers and practitioners. By improving reproducibility, the ML community can enhance the reliability and credibility of its contributions.