Urban resilience is recently a prominent issue due to rapid urbanization and increasing challenges and stressors affecting cities. Assessment of urban resilience is an essential step in enhancing resilience performance since regular assessment informs resilience action plans, determines areas of deficiencies, and provides spatial and temporal comparisons. However, resilience assessment is a complex process that requires intensive data and resources due to the multi-dimensional and dynamic nature of resilience, and the imprecision of resilience data. In this context, the research aims to develop The Resilience Performance Index (RPI), through setting a conceptual framework, defining relevant resilience indicators, and finally modelling resilience performance using The Fuzzy Logic Approach, aiming to combine resilience analysis with artificial intelligence (AI) tools and dynamic modelling methods. The RPI assesses both qualitative and quantitative resilience indicators obtained through records, census data or structured questionnaires. Indicators’ values are modelled through a designed fuzzy logic system to obtain the resilience performance score. The developed index is applied on New Damietta city to inform resilience action plans in the Nile Delta region. The RPI addresses the complexity of resilience assessment and ambiguity of resilience data through an easy applicable, user friendly approach without the need for complex mathematical and statistical methods.