Abstract

This study examines the transformative potential of big data and machine learning technologies in smart city infrastructure, analysing current integration levels, effectiveness, costs, benefits, and stakeholder perceptions. Using a mixed-methods approach with quantitative surveys and qualitative interviews, data were collected from city planners, technology providers, residents, and public health authorities across 15 smart city projects. Results indicate that data integration achieves moderate effectiveness (Grand Mean = 3.39), while machine learning demonstrates significant effectiveness in traffic management (Grand Mean = 3.45) and environmental sustainability (Grand Mean = 3.49). However, only 68% of projects implemented robust data privacy measures, revealing critical gaps in security protocols. Cost-benefit analysis shows favourable returns (Grand Mean = 3.45), though financial constraints remain a primary barrier. Qualitative analysis identified nine major themes: data privacy concerns, integration complexities, variable ML effectiveness, resource allocation challenges, scalability issues, stakeholder engagement gaps, training deficiencies, uncertainty management, and regulatory hurdles. The Technology Acceptance Model (TAM) provided theoretical framework, revealing that perceived usefulness strongly correlates with adoption rates (r = 0.78, p < 0.01), while perceived ease of use impacts implementation success (r = 0.65, p < 0.01). These findings suggest that while big data and machine learning offer substantial benefits for urban efficiency, sustainability, and service delivery, realizing their full potential requires standardized protocols, enhanced security frameworks, strategic investment planning, and comprehensive stakeholder engagement strategies. Recommendations include developing interoperability standards, investing in workforce development, implementing robust governance frameworks, and fostering public-private partnerships to bridge the digital divide and ensure inclusive smart city development.