<![CDATA[The growing volume and complexity of data in various artificial intelligence (AI) applications demand processing strategies capable of handling multilevel or hierarchical data structures. One computational approach with significant potential in this context is the recursive algorithm, which is known for its effectiveness in navigating and managing hierarchical data. This study aims to examine and integrate recursive algorithms into multilevel data processing workflows to enhance the efficiency of AI systems. By developing a processing architecture that applies recursion in key stages—such as hierarchical feature extraction, layered decision-making, and multilevel classification—this research demonstrates that recursive algorithms can simplify data complexity and improve prediction accuracy. Case studies are conducted on several applications, including document pattern recognition and hierarchical image segmentation. Experimental results show that recursive-based models outperform conventional non-recursive approaches in terms of scalability and computational efficiency. These findings reinforce the relevance of recursion in multilevel data processing and open up broader opportunities for integration within modern AI architectures.]]>
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