Abstract:

The speedy growth of Big Data revealed the inadequacy of classical computing techniques, which tend to struggle with uncertainty, noise, and high-dimensional data. Soft computing methodologies such as fuzzy logic, neural networks, and evolutionary algorithms provide tolerant, adaptive approaches to these difficulties. Unlike fixed hard classical models, soft computing provides intelligent decision-making, imprecise reasoning, and autonomous learning features, which are suited to dynamic and complicated data situations. This paper introduces a hybrid soft computing (HSC) paradigm, which combines neuro-fuzzy models, genetic algorithms, and swarm intelligence to support enhanced clustering, feature selection, and predictive modelling. Through implementation on real-world datasets in the healthcare, financial, and Internet of Things domains, we prove that there is substantial enhancement of accuracy, computation efficiency, and scalability. The suggested framework learns to adapt to changing data patterns, is more interpretable, and incurs less processing overhead than conventional analytical techniques. The results emphasize the revolutionary nature of soft computing in solving Big Data problems. Through intelligent learning and nature-inspired optimization, the techniques offer stable, scalable, and interpretable solutions, allowing for improvements in automation, decision support systems, and intelligent data analysis in diverse industries.