Reproducibility in machine learning (ML) refers to the ability to replicate the results of a model when the same input data and conditions are applied. It is a critical aspect of scientific research, ensuring that findings can be verified and built upon by others. The reproducibility crisis in ML has emerged due to challenges such as varying randomization, lack of detailed documentation, and insufficient sharing of code and datasets. Addressing these issues is essential for enhancing transparency, trust, and accountability in ML applications, ultimately leading to more reliable and effective models in real-world scenarios.