Dimensionality reduction is a fundamental technique in machine learning that simplifies datasets by reducing the number of input variables or features, enhancing computational efficiency and model performance. This simplification is crucial as datasets grow in size and complexity, introducing the "curse of dimensionality" that slows down algorithms. Dimensionality reduction transforms data into a simpler, lower-dimensional space while keeping its main features, making computation easier and lowering the risk of overfitting. Techniques like PCA, ICA, NMF, LDA, GDA, MVR, Low Variance Filter, High Correlation Filter, Forward Feature Construction, Backward Feature Elimination, and Autoencoders are used for different applications across various domains, including image and speech recognition, financial analysis, bioinformatics, genomics, and smart city solutions. These techniques help manage the "curse of dimensionality," improving model generalizability and reducing overfitting risk, contributing to better model performance and enhancing computational efficiency.