<![CDATA[Search algorithms represent a core foundational component in intelligent system design for exploring state spaces to find optimal solutions. This article presents a theoretical comparative analysis between two primary search paradigms in Artificial Intelligence (AI): uninformed search (blind search) and informed search (heuristic search), with a specific focus on problem modeling computational efficiency. The scope of this study is focused on evaluating structural parameters and time-space complexity metrics across representative algorithms, including Breadth-First Search (BFS), Depth-First Search (DFS), Uniform-Cost Search (UCS), Greedy Best-First Search, and the A* algorithm. The qualitative comparative review demonstrates that while uninformed search offers model simplicity without requiring domain knowledge, it suffers from exponential complexity growth O(bd). Conversely, informed search leveraging admissible and consistent heuristic functions radically prunes search trees and optimizes resource consumption. The main scientific contribution of this study lies in a structured evaluation framework that maps the trade-offs between heuristic informativenes]]>
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