Representation learning is a fundamental research area in machine learning that focuses on automatically discovering meaningful and compact feature representations from raw data, enabling models to capture underlying structures and improve performance across tasks. Unlike traditional hand-crafted features, deep learning-based representation learning leverages neural architectures such as autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers to extract hierarchical and task-relevant features. Early research emphasized unsupervised and self-supervised methods for dimensionality reduction and feature extraction, while recent advances explore contrastive learning, disentangled representation learning, graph representation learning, and multimodal representations. Applications span computer vision (image classification, object detection, segmentation), natural language processing (embeddings, semantic understanding, machine translation), speech recognition, bioinformatics, and recommendation systems. Current studies also investigate fairness, robustness, transferability, and interpretability of learned representations, as well as scalable frameworks for large datasets and pre-trained foundation models. These developments establish representation learning as a cornerstone of modern AI, driving progress in generalization, adaptability, and cross-domain knowledge transfer.