Abstract

The rapid proliferation of diverse and voluminous data sources has created fundamental challenges for knowledge discovery, especially in heterogeneous environments encompassing structured, semi-structured, and unstructured data. Traditional analytics methods often fall short in efficiently integrating and extracting meaningful patterns across multi-source datasets. This research explores artificial intelligence (AI)-assisted knowledge discovery frameworks designed to address these challenges by leveraging machine learning (ML), deep learning (DL), semantic integration, and data fusion techniques. We analyze the architectural elements, workflow processes, and integration mechanisms that facilitate scalable and accurate knowledge extraction. The study synthesizes insights from a comprehensive review of literature, comparing prominent frameworks and identifying strengths, limitations, and areas for improvement. Findings demonstrate that AI-assisted frameworks significantly enhance the effectiveness of discovery processes by automating feature extraction, reducing manual preprocessing, and enabling cross-modal correlation analysis. However, issues such as data inconsistency, computational complexity, and interpretability remain pressing concerns. The results of this research offer practical recommendations for developing adaptable, real-time, and scalable knowledge discovery systems, paving the way for future innovations in heterogeneous data environments.