The rapid expansion of the gig economy has significantly transformed last-mile delivery systems, driven by the increasing demand for fast, flexible, and cost-efficient logistics solutions. However, the infrastructure supporting gig-based delivery operations remains fragmented and lacks optimization, which hinders the potential of these systems. This review proposes a conceptual framework for developing data-informed infrastructure that can enhance the efficiency, sustainability, and scalability of last-mile delivery systems in the gig economy. The framework integrates key components such as data collection and management, technology infrastructure, stakeholder collaboration, and performance metrics to create an ecosystem that enables dynamic, real-time decision-making. Through the use of advanced technologies like AI, machine learning, IoT, and cloud computing, the framework facilitates better route optimization, demand forecasting, and resource allocation, which in turn improve operational efficiency. The framework also emphasizes the importance of collaboration between gig economy platforms, local governments, logistics companies, and other stakeholders, ensuring that data sharing and governance models are well-defined. Moreover, it addresses challenges such as data privacy, platform integration, and fair worker compensation, while highlighting opportunities for growth, including the potential for optimizing sustainability and expanding gig work into underserved regions. By providing a holistic view of the necessary infrastructure components and their interconnections, this conceptual framework offers a pathway to developing robust, data-driven last-mile delivery systems that are responsive, efficient, and scalable. It also sets the stage for future research into the evolving role of data in transforming urban logistics and the gig economy, and it underscores the need for a collaborative, data-centric approach to infrastructure development.