The convergence of recent advancements in electric vehicle infrastructure, urban transportation logistics, and power systems management reveals a common thread centered around the integration of sophisticated computational methods and innovative mathematical techniques to address the complexities of modern energy and transportation systems. Researchers are increasingly adopting data-driven approaches, leveraging machine learning and advanced optimization algorithms to enhance the efficiency, scalability, and sustainability of these systems. In the realm of electric vehicles, there is a notable focus on developing open-source simulation platforms for vehicle-to-grid technologies and employing stable matching algorithms for efficient charging assignment. Urban transportation logistics are benefiting from multi-modal optimization frameworks that integrate various transportation modes, employing mixed-integer linear programming and modified graph-based techniques to handle real-world constraints. Meanwhile, power systems management is seeing the adoption of end-to-end frameworks that jointly optimize system operation, incorporating physics-based models and probabilistic approaches to better manage renewable energy sources and storage systems. Noteworthy contributions include an open-source simulation platform for V2G analysis, a stable matching-based framework for EV charging assignment, a multi-modal optimization framework for shared e-mobility, and an end-to-end framework for calibrating wind power forecast models. These advancements collectively underscore a shift towards more integrated, robust, and efficient solutions that are poised to transform the landscape of energy and transportation systems.