Artificial Intelligence (AI) has emerged as a transformative force reshaping modern technology, industry, and society. This paper presents a comprehensive review of the foundational theories that underpin AI, tracing its evolution from rule-based symbolic systems to data-driven learning paradigms such as machine learning and deep learning. The review systematically examines critical application areas where AI has demonstrated significant impact, including healthcare, finance, education, transportation, and agriculture. By analyzing current trends and real-world implementations, the paper highlights the practical utility and interdisciplinary relevance of AI across diverse domains. Furthermore, it delves into the ethical considerations and technological challenges that accompany the growing deployment of intelligent systems, such as algorithmic bias, explainability, data privacy, and regulatory gaps. In addition to evaluating the state-of-the-art, the paper explores emerging directions that are likely to shape the future of AI, including neuromorphic computing, federated learning, quantum-enhanced intelligence, and the pursuit of Artificial General Intelligence (AGI). This review aims to serve as a critical resource for researchers, practitioners, and policymakers by providing a synthesized understanding of where AI stands today and where it is heading.

Keywords: Artificial Intelligence, Machine Learning, Applications, Challenges, Future Trends, Neural Networks, Ethics