This systematic review evaluates the effectiveness of Machine Learning (ML) and Deep Learning (DL) techniques in the detection and diagnosis of brain tumors compared with traditional methods. The research highlights that Convolutional Neural Network (CNN) models and DL models demonstrated their ability to identify tumors with an accuracy of over 95%, which translates into faster and more accurate diagnoses, crucial for early and effective treatment. Although ML and DL offer greater diagnostic accuracy and efficiency compared to traditional methods, challenges remain, such as the need for large, annotated datasets and the interpretability of the models. The review highlights the potential of DL to distinguish tumor types and degrees of malignancy and proposes strategies to improve the early and accurate detection of brain tumors, such as the integration of multimodal imaging data and the use of advanced preprocessing and transfer learning techniques. Future research should focus on enhancing model interpretability, robust multi-modal data fusion, and optimizing clinical workflows for ML technology adoption.