Autoencoders are a key technique in unsupervised learning for feature extraction and dimensionality reduction. This survey provides a comprehensive overview of autoencoders, their development, and applications across various domains. The paper begins with an explanation of the principles and development of conventional autoencoders, followed by a taxonomy based on their structures and principles. It thoroughly analyzes and discusses various autoencoder models, including sparse, contractive, and orthogonal autoencoders. The paper also reviews the applications of autoencoders in machine vision, natural language processing, complex networks, recommender systems, speech processing, and anomaly detection. It summarizes the limitations of current autoencoder algorithms and discusses future research directions. Autoencoders are neural networks that use backpropagation for feature learning and are primarily used for unsupervised learning tasks. They are particularly useful when labeled data is scarce or expensive to obtain. Autoencoders automatically learn relevant features from the data without the need for manual feature engineering, making them valuable for data compression, anomaly detection, and data denoising. They also contribute to privacy preservation techniques and are effective in reducing data storage requirements and enhancing interpretability. Despite their advantages, autoencoders have limitations, including sensitivity to hyperparameters, lack of robustness to noisy data, and difficulty in capturing complex, higher-order relationships in the data. Recent research has focused on addressing these issues through advancements in deep learning and autoencoder techniques, leading to the development of various specialized autoencoder architectures, such as robust, generative, convolutional, recurrent, semi-supervised, and graph autoencoders. The paper also discusses the hyperparameters of autoencoders, including the number of hidden layers, number of neurons, size of the latent space, activation functions, objective functions, optimization algorithms, learning rate, number of epochs, and batch size. These hyperparameters significantly influence the performance of autoencoders and require careful selection and tuning. The survey concludes with a discussion of the future directions in the field of autoencoders, emphasizing the need for further research to enhance their effectiveness in machine learning applications. The paper provides a comprehensive overview of autoencoders, their applications, and future research directions, making it a valuable resource for researchers and practitioners in the field of machine learning.Autoencoders are a key technique in unsupervised learning for feature extraction and dimensionality reduction. This survey provides a comprehensive overview of autoencoders, their development, and applications across various domains. The paper begins with an explanation of the principles and development of conventional autoencoders, followed by a taxonomy based on their structures and principles. It thoroughly analyzes and discusses various autoencoder models, including sparse, contractive, and orthogonal autoencoders. The paper also reviews the applications of autoencoders in machine vision, natural language processing, complex networks, recommender systems, speech processing, and anomaly detection. It summarizes the limitations of current autoencoder algorithms and discusses future research directions. Autoencoders are neural networks that use backpropagation for feature learning and are primarily used for unsupervised learning tasks. They are particularly useful when labeled data is scarce or expensive to obtain. Autoencoders automatically learn relevant features from the data without the need for manual feature engineering, making them valuable for data compression, anomaly detection, and data denoising. They also contribute to privacy preservation techniques and are effective in reducing data storage requirements and enhancing interpretability. Despite their advantages, autoencoders have limitations, including sensitivity to hyperparameters, lack of robustness to noisy data, and difficulty in capturing complex, higher-order relationships in the data. Recent research has focused on addressing these issues through advancements in deep learning and autoencoder techniques, leading to the development of various specialized autoencoder architectures, such as robust, generative, convolutional, recurrent, semi-supervised, and graph autoencoders. The paper also discusses the hyperparameters of autoencoders, including the number of hidden layers, number of neurons, size of the latent space, activation functions, objective functions, optimization algorithms, learning rate, number of epochs, and batch size. These hyperparameters significantly influence the performance of autoencoders and require careful selection and tuning. The survey concludes with a discussion of the future directions in the field of autoencoders, emphasizing the need for further research to enhance their effectiveness in machine learning applications. The paper provides a comprehensive overview of autoencoders, their applications, and future research directions, making it a valuable resource for researchers and practitioners in the field of machine learning.