Deep learning for anomaly detection is a prominent research area that focuses on identifying unusual patterns, outliers, or abnormal events in complex datasets across various domains such as cybersecurity, finance, healthcare, industrial systems, and IoT networks. Unlike traditional statistical methods, deep learning models automatically extract hierarchical features from raw data, enabling detection in high-dimensional, temporal, and heterogeneous datasets. Early approaches used autoencoders for reconstruction-based anomaly detection, while subsequent research integrated convolutional neural networks (CNNs) for spatial patterns, recurrent neural networks (RNNs) and LSTMs for temporal dependencies, and variational autoencoders (VAEs) or generative adversarial networks (GANs) for generative anomaly modeling. Recent advances explore hybrid architectures, attention mechanisms, graph neural networks (GNNs), and self-supervised or contrastive learning for enhanced detection accuracy and robustness. Applications include network intrusion detection, industrial equipment fault detection, fraud detection in finance, medical anomaly identification, and predictive maintenance. Current research also focuses on handling imbalanced datasets, improving interpretability, reducing false positives, and designing lightweight models suitable for edge and IoT deployments, positioning deep learning as a powerful tool for scalable, real-time, and accurate anomaly detection systems.