This chapter provides a comprehensive foundation for intelligent systems, tracing their conceptual origins, computational models, and learning paradigms. It begins by defining intelligent systems and outlining their key characteristics, historical development, and core learning approaches—supervised, unsupervised, and reinforcement learning—along with hybrid methods, data representation, knowledge structures, and human–AI interaction principles (Sect. 1.1). Supervised learning methods are then introduced, covering regression, classification, and key algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, and support vector machines, together with model evaluation and comparison (Sect. 1.2). Advanced supervised techniques follow, including ensemble learning, random forests, gradient boosting, hyperparameter tuning, strategies for imbalanced data, and transfer learning (Sect. 1.3). Unsupervised learning methods are presented next, focusing on clustering, dimensionality reduction, anomaly detection, and association rule discovery (Sect. 1.4), before moving to advanced approaches such as self-organizing maps, deep clustering, and manifold learning (Sect. 1.5). Chapter then explores the architecture and learning principles of perceptrons and multilayer perceptrons, highlighting representational power and training algorithms (Sect. 1.6), followed by the fundamentals of neural networks, including network types, loss functions, optimization, regularization, and convergence issues (Sect. 1.7). Specialized architectures are introduced with convolutional neural networks for image analysis and graph neural networks for structured data (Sect. 1.8). Fuzzy logic and fuzzy inference systems are examined as frameworks for reasoning with uncertainty and for integration with neural networks in control and decision support (Sect. 1.9). Chapter concludes with modern AI and emerging technologies, discussing generative AI, diffusion models, federated learning, explainable AI, and quantum machine learning as key drivers of future intelligent systems (Sect. 1.10).