Scopus Research — Jaber Hamid Hajeed

Urbanization is still accelerating worldwide, which is putting significant strain on cities' infrastructure, governance, and service delivery systems. This is putting cities under more and more pressure. SmartCityPredict's thorough architecture, created as a response to these problems, is driven by artificial intelligence (AI) and machine learning (ML). This system aims to reach the maximum feasible efficiency in delivering urban services using real-time data analytics techniques. The system uses many data sources for predictive modeling and dynamic decision-making across several urban sectors. These data sources include citizen feedback platforms, waste management units, energy grids, transportation systems, and sensor networks. SmartCityPredict guarantees low-latency analytics and scalable processing using a hybrid architecture combining edge-cloud computing paradigms with deep learning models, including LSTM, CNN, and Transformer networks. Compared to traditional models, the empirical analysis of smart city datasets that act as benchmarks shows notable improvements in the accuracy of projections, the speed with which services are delivered, and operational efficiency. SmartCityPredict provides a reasonable basis for the growth of smart cities that are intelligent, resilient, and focused on the needs of their citizens. Data-driven government and proactive urban management help to promote this basis. Focusing on integrating artificial intelligence innovation with pragmatic city planning and operations, this study contributes to the larger objective of sustainable urban transformation. The study, therefore, aims to identify means of combining these two facets. © 2025 IEEE.