Revolutionizing Big Data with AI-Driven Hybrid Soft Computing Techniques

The ever-growing complexity and scale of Big Data have rendered traditional computational approaches insufficient, driving the need for innovative AI-driven solutions. This paper presents an advanced framework that integrates artificial intelligence (AI) and machine learning (ML) with hybrid soft computing techniques, including fuzzy logic, deep neural networks, evolutionary algorithms, and swarm intelligence. These methods collectively address challenges such as high dimensionality, uncertainty, realtime processing, and scalability, thereby achieving enhanced accuracy, interpretability, and adaptability. Cutting-edge strategies, such as adaptive neuro-fuzzy systems and deep neuro-evolution, enable transformative improvements across diverse domains, including healthcare, IoT, and social media. Experimental evaluations on real-world datasets demonstrate significant advancements, including up to 20% faster processing speeds and a 15% improvement in predictive accuracy compared to traditional method s. This research underscores the pivotal role of AI-augmented soft computing in shaping the future of Big Data analytics, offering robust and scalable solutions to meet evolving industrial demands. Furthermore, it lays the foundation for developing next-generation systems capable of addressing emerging challenges in data-driven decision-making across industries.