The Unified Surface Intelligence Platform (USIP) is a machine learning decision support system designed to enhance planetary surface mission planning by leveraging multi modal data fusion. Current approaches to landing site and traverse risk assessment often rely on fragmented analysis of individual datasets, such as orbital imagery, terrain elevation, thermal inertia, and historical rover performance, requiring extensive manual coordination across specialized teams. USIP address this limitation by integrating heterogeneous planetary datasets into a unified probabilistic framework. By applying cross domain pattern recognition, the system learns relationships between environmental conditions, terrain features, and historical mission outcomes to generate interpretable, ranked risk assessments for candidate landing sites and traversal paths. This multi modal approach enables more comprehensive situational awareness compared to single source analysis. A key innovation of USIP is its ability to infer risk in previously unexplored regions by identifying patterns across combined datasets, rather than relying solely on direct prior observations. The system produces risk maps and decision support outputs that can assist mission planners in evaluating landing safety, optimizing traverse routes, and improving operational efficiency. As a proof of concept, USIP is designed to utilize existing NASA planetary datasets and validate its predictions against known mission outcomes, such as rover entrapment events and environmental hazards. By reducing analysis time and improving risk interpretability, USIP has the potential to support faster, more informed decision making in future lunar and planetary surface missions.