The growing need for accurate diagnosis and treatment planning has driven advancements in brain tumor image reconstruction and analysis through medical imaging. Recent progress in machine learning, particularly deep learning models, has significantly enhanced image processing capabilities. This review explores various machine learning algorithms applied to brain tumor image analysis and reconstruction across imaging modalities such as PET, CT, and MRI, with a focus on strategies to improve image clarity, reduce artifacts, and enhance quality in low-dose imaging. Techniques like GANs, autoencoders, and U-Net have shown promise in tasks such as denoising and segmentation but often face challenges due to high computational costs and the need for large training datasets. Despite these advancements, issues with model generalizability and fine-tuning requirements pose barriers to clinical implementation. The review highlights the trade-off between computational efficiency and precision, suggesting that further research is needed to address these challenges and improve the clinical applicability of these models.