The development of the Internet of Things (IoT), web technology, cloud computing, digital twins, and data analytics has driven a major transformation in the transportation and fleet management sectors. This study presents a Systematic Literature Review (SLR) of 20 international publications from 2020–2025 that highlight the integration of web technologies (REST APIs, web dashboards, cloud platforms), IoT sensors, telematics, digital twins, and machine learning in smart transportation and fleet management. The SLR process followed the PRISMA steps, starting with the identification of 842 articles, title/abstract selection, eligibility assessment, and final screening. The results reveal five main focuses: (1) IoT-based sensing & monitoring, (2) Web-based fleet dashboards & APIs, (3) Data analytics & predictive maintenance, (4) Digital twin-enabled transportation optimization, and (5) Security & interoperability. Cluster analysis reveals a shift in innovation from simply vehicle tracking to real-time data-driven systems and digital twins. This research produces a conceptual model of digital transformation that integrates web technologies, analytics, IoT, and digital twins to support intelligent transportation systems. The study also identifies gaps such as sensor security, web services interoperability, and the lack of large-scale implementation.
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