This study presents a novel approach to evaluate public space quality in Tunisia using artificial intelligence, and Google Street View imagery. Overcoming the subjectivity and limited scope of traditional methods, it employs the YOLO v8 model to detect urban features and integrates a Sustainable Development Index with an Ecological Impact Index to quantify quality across environmental, social, and economic dimensions. The methodology involves image collection, processing, model training, and sustainability analysis. Findings show regional disparities: some Tunisian governorates balance development well, while others falter under ecological stress like waste and pollution, highlighting the need for environmental consideration. The YOLO v8 model performs strongly on distinct objects but struggles with similar classes, indicating refinement potential. This scalable, objective tool aids urban planners and local authorities in targeting interventions and supports integrated policies. Adaptable globally, it merges technology and sustainability to redefine public space assessment, stressing ecological harmony with socio-economic growth for resilient, inclusive cities.