Hybrid soft computing techniques combine fuzzy logic, artificial neural networks, and evolutionary algorithms to solve complex real world problems effectively. Individual soft computing methods provide flexibility, learning ability, and optimization capability, but each has certain limitations when used independently. Hybridization integrates their strengths to improve accuracy, adaptability, and robustness. This paper presents the fundamentals of soft computing, discusses major hybrid models such as neuro fuzzy systems, genetic fuzzy systems, and neuro genetic systems, and highlights their applications in predictive modeling, control systems, optimization, and pattern recognition. The paper also addresses key challenges including computational complexity, scalability, and interpretability.