Deep learning (DL), a sophisticated subset of machine learning (ML), has emerged as a transformative force within the broader realm of artificial intelligence (AI). By leveraging architectures such as convolutional neural networks (CNNs), DL has significantly advanced image recognition capabilities, enabling systems to identify and classify visual data with remarkable precision accurately. This technology is not only applicable to image recognition. Still, it has also made strides in diverse areas, such as speech recognition, language translation, automated gameplay, healthcare diagnostics, and the development of self-driving vehicles. The success of DL in this domain can be attributed to its ability to learn hierarchical representations of data, allowing for improved feature extraction and pattern recognition. Despite its impressive performance, deep learning is not without its limitations. Key challenges include its reliance on vast amounts of labelled data, which can be difficult and expensive to obtain, its lack of common sense reasoning and difficulties in addressing complex, multifaceted problems.Additionally, DL models often struggle with long-term planning and decision-making, which can hinder their effectiveness in certain applications. This paper delves into the significant role of deep learning in image recognition, providing a comprehensive overview of its methodologies, applications, strengths, and limitations. By examining current advancements and ongoing challenges, this work aims to contribute to understanding deep learning's impact on the field and its future potential
