Adaptive Neuro-Fuzzy Inference System (ANFIS) is a powerful hybrid intelligent technique that combines the qualitative reasoning of fuzzy logic with the learning capability of artificial neural networks. By employing a structured fuzzy inference mechanism and data-driven parameter adaptation, ANFIS can effectively model and control complex nonlinear systems under uncertainty. This work presents an overview of the ANFIS architecture, learning algorithm, and operational principles, highlighting its advantages over conventional control approaches. The suitability of ANFIS for control applications is emphasized, particularly in comparison with classical PID, fuzzy logic controllers, and neural-network-based methods. Owing to its adaptability, robustness, and improved dynamic performance, ANFIS has emerged as an effective solution for intelligent control and system identification problems in modern engineering applications.