Recent studies demonstrate promising efficacy with immune checkpoint inhibitors (ICI) for brain metastases (BM), an unmet need in modern oncology. However, a predictive biomarker for ICI efficacy is needed to inform precision-based use of ICI given its high toxicity rate. Here, we present several multimodal deep learning (DL) approaches that integrate pre-treatment magnetic resonance imaging (MRI) and clinical metadata to predict ICI efficacy for BM. Using a multi-institutional dataset of 548 patients, our best-performing models achieve an AUROC of 0.674 (±0.041). In future work, we will accrue additional clinical and radiologic data to improve performance. Furthermore, our work thus far will serve as a baseline by which to trial alternate fusion strategies to improve and refine multimodal biomarker discovery for precision oncology.