In the era of rapidly evolving transportation systems, the integration of data science methodologies offers unprecedented potential for enhancing efficiency, safety, and sustainability. This research proposes an integrated data science framework tailored to address the latest challenges in transportation systems. Focusing on real-time optimization, the framework leverages advanced analytics to dynamically adapt to changing conditions, enabling proactive traffic management and resource allocation. Moreover, a paramount emphasis is placed on the ethical handling of transportation data, ensuring privacy and fairness for all stakeholders involved. One of the pivotal aspects of this framework is its capacity to seamlessly amalgamate heterogeneous data sources, ranging from GPS devices to IoTsensors, thereby facilitating comprehensive and accurate insights. Data quality and standardization are addressed through robust protocols, guaranteeing the reliability of information for critical decision-making processes. Predictive modeling techniques are employed to anticipate traffic patterns and demand, optimizing routes and resource allocation in real time. However, the implementation of such an integrated framework is not without its challenges. Privacy concerns, cybersecurity threats, and ethical considerations demand meticulous attention. This research delves into these issues, proposing measures to safeguard sensitive information and ensure equitable outcomes. Through this comprehensive framework, the research aims to revolutionize the landscape of transportation systems, providing a holistic solution that optimizes operations while upholding the highest standards of ethical conduct and data security.