<![CDATA[Search algorithms are a fundamental component in computer science, specifically in the domain of Artificial Intelligence (AI) for solving state space search problems. This study aims to conduct a comparative analysis between Uninformed Search strategies (BFS, DFS) and Informed Search strategies (A*, Hill Climbing, Simulated Annealing). The research method used is a Systematic Literature Review (SLR) by synthesizing data from primary and secondary sources. The results indicate a significant trade-off; Uninformed Search such as BFS guarantees optimality but has high space complexity while DFS is memory efficient but not complete. Conversely, Informed Search significantly increases efficiency, requiring only about 4.45% of computation compared to blind search. The A* algorithm is identified as the most effective strategy for pathfinding by balancing actual cost and heuristic estimation, whereas Simulated Annealing overcomes the local optima problem found in Hill Climbing. The selection of the right algorithm depends on the specific constraints of the problem faced.]]>
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