This survey provides a comprehensive overview of convolutional neural networks (CNNs) in deep learning, focusing on their applications, challenges, and future trends. CNNs, a subset of deep learning, are widely used in computer vision tasks such as image classification, object detection, and image segmentation. Various types of CNNs, including 1D, 2D, 3D, dilated, grouped, attention, depthwise, and NAS, have been developed to meet specific needs. Each type has unique structures and characteristics, making them suitable for different tasks. Understanding and comparing these CNN types is crucial for identifying their strengths and weaknesses and for developing new and improved architectures. The survey discusses the platforms and frameworks used by researchers, as well as the main research fields of CNNs, such as 6D vision, generative models, and meta-learning. It also explores the challenges and future directions for CNNs, including energy efficiency, computational complexity, and the need for lightweight and efficient architectures. The paper highlights the importance of convolutional operations in deep learning, particularly in processing data organized in grid-like structures such as images, audio signals, and sequential data. Convolutional operations enable the extraction of essential features from input data, making CNNs highly effective for image analysis tasks. The survey also addresses the challenges of CNNs, including computational complexity and the need for efficient architectures. It discusses techniques such as lightweight CNN architectures and compression methods to improve energy efficiency and reduce computational complexity. The paper emphasizes the importance of understanding the different types of convolutions and their applications in various AI tasks. It also explores the evolution of CNN architectures, including Inception modules, ResNets, DenseNets, and MobileNets, which have significantly improved the performance and efficiency of CNNs. The survey provides a detailed overview of advanced convolutional techniques, including transposed convolutions, depthwise separable convolutions, spatial pyramid pooling, and attention mechanisms. These techniques have been developed to address specific challenges in CNNs, such as handling varying input sizes, improving feature extraction, and enhancing model performance. The paper also discusses recent advancements in CNNs, including capsule networks, neural architecture search, and generative adversarial networks (GANs), which have revolutionized the field of deep learning and image generation. Overall, this survey aims to provide a comprehensive understanding of CNNs, their applications, challenges, and future trends, helping researchers and practitioners to design efficient and effective models for a wide range of applications.This survey provides a comprehensive overview of convolutional neural networks (CNNs) in deep learning, focusing on their applications, challenges, and future trends. CNNs, a subset of deep learning, are widely used in computer vision tasks such as image classification, object detection, and image segmentation. Various types of CNNs, including 1D, 2D, 3D, dilated, grouped, attention, depthwise, and NAS, have been developed to meet specific needs. Each type has unique structures and characteristics, making them suitable for different tasks. Understanding and comparing these CNN types is crucial for identifying their strengths and weaknesses and for developing new and improved architectures. The survey discusses the platforms and frameworks used by researchers, as well as the main research fields of CNNs, such as 6D vision, generative models, and meta-learning. It also explores the challenges and future directions for CNNs, including energy efficiency, computational complexity, and the need for lightweight and efficient architectures. The paper highlights the importance of convolutional operations in deep learning, particularly in processing data organized in grid-like structures such as images, audio signals, and sequential data. Convolutional operations enable the extraction of essential features from input data, making CNNs highly effective for image analysis tasks. The survey also addresses the challenges of CNNs, including computational complexity and the need for efficient architectures. It discusses techniques such as lightweight CNN architectures and compression methods to improve energy efficiency and reduce computational complexity. The paper emphasizes the importance of understanding the different types of convolutions and their applications in various AI tasks. It also explores the evolution of CNN architectures, including Inception modules, ResNets, DenseNets, and MobileNets, which have significantly improved the performance and efficiency of CNNs. The survey provides a detailed overview of advanced convolutional techniques, including transposed convolutions, depthwise separable convolutions, spatial pyramid pooling, and attention mechanisms. These techniques have been developed to address specific challenges in CNNs, such as handling varying input sizes, improving feature extraction, and enhancing model performance. The paper also discusses recent advancements in CNNs, including capsule networks, neural architecture search, and generative adversarial networks (GANs), which have revolutionized the field of deep learning and image generation. Overall, this survey aims to provide a comprehensive understanding of CNNs, their applications, challenges, and future trends, helping researchers and practitioners to design efficient and effective models for a wide range of applications.