Acute ischemic stroke is a critical medical emergency caused by a sudden loss of blood flow to the brain, leading to brain cell death due to a lack of oxygen and nutrients. While Diffusion Weighted Imaging (DWI) is instrumental in diagnosing ischemic stroke, current practices primarily rely on the manual assessment of DWI images to delineate the ischemic core, a time-intensive and subjective process that introduces variability in clinical decisions regarding endovascular treatments. This study aims to address these limitations by evaluating artificial intelligence (AI)-driven methods for automated DWI analysis, focusing on their potential to enhance stroke outcome prediction accuracy and consistency. Using a systematic review methodology, we examine AI applications that automatically identify stroke-affected areas in DWI scans and compare key approaches in terms of their performance metrics. Our findings indicate that AI methodologies can improve the speed and precision of stroke assessments, suggesting that these technologies have significant implications for advancing diagnostic consistency and aiding clinical decision-making in stroke care.