<![CDATA[This study examines the effectiveness of the Early Warning System (EWS) in anticipating and responding to mental health crises in conflict-affected regions of the Middle East through a systematic review of 47 scholarly articles published between 2014 and 2024. The meta-regression findings indicate a significant contribution of EWS implementation to the reduction of post-traumatic stress disorder (PTSD) symptoms with a coefficient of β = -0.67 (p < .001), as well as depressive symptoms with a coefficient of β = -0.59 (p < .001) among populations directly affected by armed conflict. Among 12,456 respondents analysed, 73.8% reported a reduction in anxiety symptoms following the implementation of EWS, with an effect size of d = 0.82 (95% CI [0.76, 0.88]). Digitally based early warning systems demonstrated a significantly higher level of effectiveness (OR = 2.34, 95% CI [1.98, 2.70]) than conventional systems, which are more manual and reactive. Moderator analysis indicated that age (β = -0.31, p < .01) and the duration of exposure to conflict (β = 0.44, p < .001) play important roles in moderating the relationship between EWS interventions and various mental health indicators. These findings expand upon the conclusions of Fu et al. (2020) and Salesi (2023), which previously explored psychosocial interventions in conflict zones, by adding a new dimension—examining digital technology and predictive algorithms within EWS frameworks. The study explicitly demonstrates that integrating machine learning models into EWS can enhance the predictive accuracy of potential mental health crises to 84.6%, representing a novel contribution that has not been comprehensively documented in prior academic literature]]>
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