This paper provides a comprehensive review of deep learning (DL), covering its concepts, architectures, challenges, applications, and future directions. DL has emerged as the gold standard in machine learning (ML), achieving outstanding results on complex cognitive tasks and outperforming traditional ML techniques in various domains such as cybersecurity, natural language processing, bioinformatics, robotics, and medical information processing. The review aims to provide a holistic understanding of DL by discussing its importance, types of techniques and networks, convolutional neural networks (CNNs), and their development, challenges, and solutions. It also covers major DL applications, computational tools like FPGA, GPU, and CPU, and evaluates their influence on DL. The paper concludes with an evolution matrix, benchmark datasets, and a summary of key findings. The authors emphasize the need for a deep understanding of DL to advance research and applications in various fields.This paper provides a comprehensive review of deep learning (DL), covering its concepts, architectures, challenges, applications, and future directions. DL has emerged as the gold standard in machine learning (ML), achieving outstanding results on complex cognitive tasks and outperforming traditional ML techniques in various domains such as cybersecurity, natural language processing, bioinformatics, robotics, and medical information processing. The review aims to provide a holistic understanding of DL by discussing its importance, types of techniques and networks, convolutional neural networks (CNNs), and their development, challenges, and solutions. It also covers major DL applications, computational tools like FPGA, GPU, and CPU, and evaluates their influence on DL. The paper concludes with an evolution matrix, benchmark datasets, and a summary of key findings. The authors emphasize the need for a deep understanding of DL to advance research and applications in various fields.