With the acceleration of urbanization, there is an increasing need for sustainable, efficient mobility systems that promote walking as a primary method of transportation. However, a significant challenge persists: the absence of pedestrian-friendly walking environments and infrastructure in most developing countries due to the lack of up-to-date data and information necessary for deriving sustainable solutions. In this study, we present a big data-driven AI framework for detecting pedestrian hotspots by integrating smartphone sensor-collected GPS data, unsupervised machine learning, and geospatial analysis. Identifying the areas, locations, and hotspots with high pedestrian concentration is important to create smart solutions for addressing the issues in those hotspots to improve sustainable development. This framework is an innovative and quantitative method that includes location-based service (LBS) data and employs data clustering with machine-learning techniques. To identify the high pedestrian concentration hotspot, this study focused on three major criteria based on the literature review, which are walking speed, walking time, and walking direction inside the experimental case study area. Utilizing k-means clustering, 622-speed clusters were established. Geospatial analysis methods were employed to identify areas requiring targeted interventions to promote walking, thereby reducing dependence on motor vehicles, mitigating climate change effects, and fostering the development of green cities. This study creates a framework that links academic research with real-world applications. It shows how GIS service providers and policymakers can use AI techniques and real-time data to make cities walkable and pedestrian-friendly, especially when it comes to building transportation infrastructure and smart navigation systems. This study’s results contribute to the achievement of Sustainable Development Goal 11, which focuses on inclusive and safe urban environments as well as sustainable cities and communities. This initiative further supports SDG 13 by contributing to the reduction of carbon emissions and the mitigation of climate change, as well as SDG 9, which focuses on industry, innovation, and infrastructure, and SDG 3, which emphasizes health and well-being.