This paper provides a comprehensive survey of Convolutional Neural Networks (CNNs), covering their history, architecture, key models, applications, and future directions. CNNs, a type of deep neural network, have become one of the most significant models in deep learning due to their success in tasks like computer vision and natural language processing. The paper aims to provide a general overview of CNNs, including their basic components, key innovations, and applications across different dimensions (1D, 2D, and multi-dimensional). It also discusses the challenges and promising directions for future research. The paper begins with an introduction to the history of CNNs, tracing their development from early neural networks to modern deep learning models. It then provides an overview of CNN architecture, including convolution, activation functions, pooling, and other key components. The paper introduces several classic and advanced CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, ResNet, DCGAN, MobileNet, and ShuffleNet, highlighting their innovations and contributions to the field. It also discusses various types of convolutions, including separable convolutions, group convolutions, and multi-dimensional convolutions. The paper covers the applications of CNNs in different dimensions, including 1D, 2D, and multi-dimensional convolutions, and discusses the challenges and future directions of CNN research. It emphasizes the importance of activation functions, loss functions, and optimizers in CNN performance, and provides experimental analysis to support these points. The paper concludes with a discussion of the current state of CNN research, highlighting the need for further exploration in areas such as model efficiency, accuracy, and generalization. The paper also provides some rules of thumb for selecting appropriate activation functions, loss functions, and optimizers based on the specific task and data. Overall, the paper aims to provide a comprehensive understanding of CNNs and their potential for future research and applications.This paper provides a comprehensive survey of Convolutional Neural Networks (CNNs), covering their history, architecture, key models, applications, and future directions. CNNs, a type of deep neural network, have become one of the most significant models in deep learning due to their success in tasks like computer vision and natural language processing. The paper aims to provide a general overview of CNNs, including their basic components, key innovations, and applications across different dimensions (1D, 2D, and multi-dimensional). It also discusses the challenges and promising directions for future research. The paper begins with an introduction to the history of CNNs, tracing their development from early neural networks to modern deep learning models. It then provides an overview of CNN architecture, including convolution, activation functions, pooling, and other key components. The paper introduces several classic and advanced CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, ResNet, DCGAN, MobileNet, and ShuffleNet, highlighting their innovations and contributions to the field. It also discusses various types of convolutions, including separable convolutions, group convolutions, and multi-dimensional convolutions. The paper covers the applications of CNNs in different dimensions, including 1D, 2D, and multi-dimensional convolutions, and discusses the challenges and future directions of CNN research. It emphasizes the importance of activation functions, loss functions, and optimizers in CNN performance, and provides experimental analysis to support these points. The paper concludes with a discussion of the current state of CNN research, highlighting the need for further exploration in areas such as model efficiency, accuracy, and generalization. The paper also provides some rules of thumb for selecting appropriate activation functions, loss functions, and optimizers based on the specific task and data. Overall, the paper aims to provide a comprehensive understanding of CNNs and their potential for future research and applications.