Introduction: Evaluating the performance of Mental Health (MH) services is a recent research focus, often relying on resource and utilization rates within geographical areas for an MH reference service. Andalusian MH in Spain adopts a community-based care model for service integration. Due to the absence of clear efficiency score cut-offs, determining statistically significant differences among units for each care type poses a challenge. This research aims to understand service complementarity and identify spatial units with high (hotspots) and low (coldspots) performance in Andalusia. Methods: This study analyzed small mental health areas in Andalusia using a hybrid model with Monte-Carlo simulations, artificial intelligence, and Data Envelopment Analysis. Input variables included services, the number of beds/places, psychiatrists, nurses, and total professionals in outpatient, acute hospital, and day care services. Outputs were the number of visits (frequentation) and the total cases (prevalence) treated in outpatient care. Four scenarios assessed the potential balance of care in outpatient community care by integrating acute hospital and day care. Model results were analyzed with global and local autocorrelation indexes, identifying spatial clusters and projecting them on maps. Results: The findings comprised maps illustrating RTE score distribution and another set revealing hotspots and coldspots for each scenario. Spatial clusters were identified at both global and local scales throughout Andalusia. Local clusters were found for outpatient care (Baseline – Scenario 1) and outpatient and acute hospital care (Scenario 2), while global and local clusters were identified for outpatient and day care (Scenario 3) and all three care types together (Scenario 4). Discussion: The results identify areas with significantly different types of care integration and performance, assisting planners and decision-makers in pursuing efficiency, quality, and equity in mental health care.