Artificial Intelligence (AI) is the broadest of the three terms, referring to the capability of a machine to perform tasks typically requiring human intelligence. Machine Learning (ML) is a subfield of AI that focuses specifically on algorithms and statistical models that enable machines to improve their performance on a given task through experience. Deep Learning (DL), on the other hand, represents a more specialized subset of ML that utilizes neural networks with many layers to perform tasks like image recognition, natural language processing, and scientific image analysis. While AI is broad in scope, ML relies heavily on labeled data to learn patterns and make predictions, whereas DL introduces additional challenges due to its reliance on intricate architectures and massive computational needs. Despite these complexities, the applications of AI, ML, and DL span a multitude of industries, transforming how we interact with technology, from voice assistants and recommendation systems to self-driving cars and medical diagnosis. However, several challenges complicate the adoption and effectiveness of these technologies, including computational complexity, lack of support and awareness, black-box nature, data privacy concerns, data sparsity, and the need for explainability enhancements. As the growth trajectory of AI, ML, and DL continues to gain momentum, recent trends include increased investment in AI start-ups, AI in customer engagement, and boosting productivity through automation.