In the age of big data, dimensionality reduction has emerged as a fundamental technique for tackling the issues associated with high-dimensional datasets, especially in medical data processing. This chapter offers a comprehensive examination of essential dimensionality reduction methods, such as PCA, LDA, t-SNE, UMAP, and Autoencoders. These techniques are crucial for alleviating computational inefficiencies, overfitting, and interpretability challenges associated with high-dimensional data. PCA and LDA are proficient in linear dimensionality reduction, whereas t-SNE and UMAP provide robust methodologies for visualizing intricate, nonlinear systems. Autoencoders, utilizing neural networks, offer a versatile framework for identifying complex patterns in multimodal data. The review underscores their theoretical foundations, practical applications, and comparative advantages, accentuating their essential role in improving the efficiency, accuracy, and interpretability of artificial intelligence models in healthcare. Future research may concentrate on hybrid methodologies and scalable solutions to tackle emerging issues in medical imaging and related fields.