The challenges of sustainable urban development increases daily and the usual planning approaches to ameliorate these challenges are ineffective. This study assesses the applicability of ‘smart infrastructure’ to achieve sustainable urban development in Lagos metropolis. The study theoretical framework is based on ‘smart’ principles. The study adopts a mixed method of data collection and analysis with multi-stage sampling techniques of 460 households in six communities of Eti-Osa and Ikeja local government areas of Lagos metropolis. The objectives of the study were to examine the socioeconomic characteristics of the residents and assess the infrastructure, the building uses and the environmental conditions of the study areas in relation to smart principles and smart infrastructure application in the study areas. The data were analyzed using inferential statistics and the findings reflect that the building use is sprawl development in nature with the informal settlement and infrastructure inadequacy as the major challenges. Smart infrastructure approaches are identified as applicable for achieving sustainable development in the study areas with smart interventions in socioeconomic status of the residents, the environment, the building uses and the basic facilities and services in the study areas

University towns face many challenges in the 21st century due to urbanization, increased student population, and higher educational institutions’ inability to house all their students on-campus. For university towns to be resilient and sustainable, the challenges facing them must be assessed and addressed. To carry out community resilience assessments, this study adopted a novel methodological framework to harness the power of artificial intelligence and social media big data (user-generated content on Twitter) to carry out remote studies in six university towns on six continents using Text Mining, Machine Learning, and Natural Language Processing. Cultural, social, physical, economic, and institutional and governance community challenges were identified and analyzed from the historical big data and validated using an online expert survey. This study gives a global overview of the challenges university towns experience due to studentification and shows that artificial intelligence can provide an easy, cheap, and more accurate way of conducting community resilience assessments in urban communities. The study also contributes to knowledge of research in the new normal by proving that longitudinal studies can be completed remotely.