Smart decision-making on bridge maintenance is critical to ensure public safety and optimal resource reallocation. In the last decade, artificial intelligence (AI)-based systems have been developed and trained on data derived from dedicated sensor networks that typically measure structural vibration responses such as accelerations and strains. Although such systems hold promise and demonstrate efficacy in field settings, their implementation is not ubiquitous due to several open challenges, such as the finances and human resources necessary for the scalable implementation and maintenance of such systems. In addition to financial resource limitations, the data requirements for AI models are also significant, which is a major drawback for vibration-based infrastructure monitoring paradigms. Mobile sensing has recently been explored as an alternative to address scalability. Furthermore, crowdsourcing mobile sensing data has been proposed to obtain substantial data volumes as well. This study investigates crowdsourced data streams from mobile sensors from a micromobility perspective, discussing how advancements in sensing technologies, sensor networks, and data analytics can create new opportunities for AI-driven bridge Structural Health Monitoring (SHM) systems to assess bridge integrity. The study also presents a roadmap for bridging the gap between advanced sensing technologies and AI integration in SHM applications.