Despite their transformative potential, deep learning and machine learning models come with several limitations. One significant drawback is the requirement for large datasets; many models, especially deep learning architectures, need extensive labeled data to perform effectively. This can be a barrier in industries where data is scarce or difficult to obtain. To address this, techniques such as data augmentation, transfer learning, and semi-supervised learning can be employed to enhance model performance with limited data. Another limitation is the black-box nature of many deep learning models, which makes it challenging to interpret their decision-making processes. This lack of transparency can hinder trust and adoption in critical applications like healthcare and finance. To mitigate this, researchers are developing explainable AI (XAI) techniques that aim to provide insights into model predictions. Additionally, issues related to bias and fairness in machine learning models can lead to discriminatory outcomes. Addressing this requires implementing robust data governance practices, conducting bias audits, and ensuring diverse representation in training datasets.