Urban districts affected by social fragility and environmental degradation increasingly require predictive and adaptive tools capable of addressing intensifying climate pressures and complex ecological dynamics. This contribution presents a multilayer framework for assessing and enhancing urban environmental resilience by integrating AI-driven environmental analysis, distributed sensing networks and predictive ecological modelling. The proposed approach correlates real-time data on air quality, urban microclimate, mobility flows, land use and public-space conditions with socio-territorial indicators, enabling the early detection of environmental stress patterns in vulnerable neighbourhoods. Machine learning algorithms are employed to identify latent ecological relationships and support dynamic, data-driven interpretations of urban ecosystem behaviour. Experimental application of the framework, based on simulated scenarios and existing urban datasets, indicates a potential 20–25% reduction in urban heat island intensity, improvements in local ecological continuity, and an increase of over 30% in environmental risk prediction accuracy when compared to conventional static assessment methods. These quantitative results demonstrate how AI-based models can effectively support targeted interventions and evidence-based adaptive resilience strategies. The proposed model integrates environmental and socio-territorial indicators within a single dynamic predictive system, oriented towards decision support for urban resilience. By integrating quantitative indicators into adaptive decision-making processes, the research contributes to advancing next-generation approaches for managing environmental vulnerability in contemporary cities.