(untitled) (Al-Farooq Journal of Sciences, 2026)
Artificial Intelligence (AI) has undergone a remarkable transformation, evolving from symbolic reasoning systems to data-driven machine intelligence. Early AI systems relied on explicit rule-based knowledge representations that were transparent, logical, and interpretable. However, they lacked adaptability, scalability, and learning capability. The emergence of machine learning enabled AI systems to identify patterns from data and improve performance through experience rather than depending solely on expert-defined rules. This transition was further accelerated by artificial neural networks and deep learning, which leverage large-scale data and computational power to learn complex representations that often outperform manually engineered features. This paper presents a comparative analysis of the principal paradigms of artificial intelligence: Symbolic AI, Machine Learning, and Deep Learning, based on explain ability, scalability, data dependency, and suitability for real-world applications. In addition, a conceptual Hybrid AI framework is proposed to integrate reasoning and learning in a unified architecture that balances performance with interpretability. The study aims to support researchers and practitioners in selecting suitable AI paradigms for modern intelligent systems and highlights the growing importance of explainable, reliable, and human-centered AI.