This survey provides a comprehensive overview of deep learning-based anomaly detection (DAD) techniques, their applications, and challenges. Anomaly detection aims to identify instances that deviate significantly from normal behavior, often indicating errors, fraud, or novel patterns. Deep learning has shown superior performance in anomaly detection compared to traditional methods, especially as data scales. The survey categorizes DAD techniques into supervised, semi-supervised, and unsupervised approaches based on label availability and training objectives. Supervised methods use labeled data to learn separating boundaries, while semi-supervised methods leverage a small amount of labeled data alongside unlabeled data. Unsupervised methods detect anomalies without prior labels, relying on intrinsic data properties. Hybrid models combine deep learning with traditional anomaly detection techniques, and one-class neural networks focus on learning normal data distributions to identify outliers. The survey also discusses the challenges of detecting anomalies in various domains, including intrusion detection, fraud detection, healthcare, and IoT. Key techniques include autoencoders, recurrent neural networks, and deep belief networks. The survey highlights the importance of computational efficiency, interpretability, and adaptability in real-world applications. It also addresses the limitations of current methods, such as class imbalance and the need for large-scale data. The survey concludes that deep learning-based anomaly detection is a promising area with significant potential for future research and practical implementation.This survey provides a comprehensive overview of deep learning-based anomaly detection (DAD) techniques, their applications, and challenges. Anomaly detection aims to identify instances that deviate significantly from normal behavior, often indicating errors, fraud, or novel patterns. Deep learning has shown superior performance in anomaly detection compared to traditional methods, especially as data scales. The survey categorizes DAD techniques into supervised, semi-supervised, and unsupervised approaches based on label availability and training objectives. Supervised methods use labeled data to learn separating boundaries, while semi-supervised methods leverage a small amount of labeled data alongside unlabeled data. Unsupervised methods detect anomalies without prior labels, relying on intrinsic data properties. Hybrid models combine deep learning with traditional anomaly detection techniques, and one-class neural networks focus on learning normal data distributions to identify outliers. The survey also discusses the challenges of detecting anomalies in various domains, including intrusion detection, fraud detection, healthcare, and IoT. Key techniques include autoencoders, recurrent neural networks, and deep belief networks. The survey highlights the importance of computational efficiency, interpretability, and adaptability in real-world applications. It also addresses the limitations of current methods, such as class imbalance and the need for large-scale data. The survey concludes that deep learning-based anomaly detection is a promising area with significant potential for future research and practical implementation.