This comprehensive survey of Convolutional Neural Networks (CNNs) in Deep Learning (DL) explores various types of CNNs, their applications, challenges, and future trends. CNNs are widely used for computer vision tasks such as image classification, object detection, and image segmentation. The paper discusses different types of CNNs, including 1D, 2D, and 3D convolutions, as well as advanced techniques like dilated, grouped, attention, depthwise convolutions, and NAS. Each type of CNN has unique structures and characteristics, making them suitable for specific tasks. The survey highlights the strengths and weaknesses of these architectures and provides a comparative analysis to help in the development of new and improved architectures. The paper also delves into the performance, limitations, and practical applications of each type of CNN, emphasizing the importance of understanding their architectural differences. It explores platforms and frameworks used by researchers and discusses research fields like 6D vision, generative models, and meta-learning. The survey aims to address gaps in previous work by proposing a taxonomy to classify CNN architectures based on their intrinsic design patterns. Key contributions of the survey include a detailed analysis of various CNN models, a discussion of recent developments, and an exploration of future trends and open questions. The paper covers the fundamentals of convolutions, the basic components of CNNs, and advanced convolutional techniques such as transposed convolutions, depthwise separable convolutions, spatial pyramid pooling, and attention mechanisms. It also examines the evolution of CNN architectures, including Inception modules, ResNets, DenseNets, MobileNets, and EfficientNets. The applications of different convolution types are discussed, showcasing their utility in image recognition, object detection, NLP, audio processing, and medical image analysis. The paper concludes with a discussion on the performance considerations, platforms used by researchers, and popular research fields, providing a comprehensive overview of the state-of-the-art in CNNs.This comprehensive survey of Convolutional Neural Networks (CNNs) in Deep Learning (DL) explores various types of CNNs, their applications, challenges, and future trends. CNNs are widely used for computer vision tasks such as image classification, object detection, and image segmentation. The paper discusses different types of CNNs, including 1D, 2D, and 3D convolutions, as well as advanced techniques like dilated, grouped, attention, depthwise convolutions, and NAS. Each type of CNN has unique structures and characteristics, making them suitable for specific tasks. The survey highlights the strengths and weaknesses of these architectures and provides a comparative analysis to help in the development of new and improved architectures. The paper also delves into the performance, limitations, and practical applications of each type of CNN, emphasizing the importance of understanding their architectural differences. It explores platforms and frameworks used by researchers and discusses research fields like 6D vision, generative models, and meta-learning. The survey aims to address gaps in previous work by proposing a taxonomy to classify CNN architectures based on their intrinsic design patterns. Key contributions of the survey include a detailed analysis of various CNN models, a discussion of recent developments, and an exploration of future trends and open questions. The paper covers the fundamentals of convolutions, the basic components of CNNs, and advanced convolutional techniques such as transposed convolutions, depthwise separable convolutions, spatial pyramid pooling, and attention mechanisms. It also examines the evolution of CNN architectures, including Inception modules, ResNets, DenseNets, MobileNets, and EfficientNets. The applications of different convolution types are discussed, showcasing their utility in image recognition, object detection, NLP, audio processing, and medical image analysis. The paper concludes with a discussion on the performance considerations, platforms used by researchers, and popular research fields, providing a comprehensive overview of the state-of-the-art in CNNs.