Deep autoencoders are a key research area in deep learning, focusing on unsupervised neural network architectures designed for learning efficient, low-dimensional representations of high-dimensional data. Research papers in this domain explore applications such as anomaly detection, dimensionality reduction, image and video reconstruction, denoising, feature extraction, IoT data compression, and predictive modeling in healthcare, finance, and cybersecurity. Key contributions include variations like stacked autoencoders, denoising autoencoders, variational autoencoders (VAEs), sparse autoencoders, and hybrid models integrating convolutional or recurrent layers to handle structured, sequential, or multimedia data. Recent studies also address challenges such as overfitting, scalability for large datasets, training stability, interpretability of latent features, and deployment in resource-constrained environments. By leveraging deep autoencoders, research aims to provide robust, adaptive, and efficient feature learning and reconstruction capabilities, enabling improved performance in downstream machine learning and analytical tasks.