Multi-modal machine learning (MMML) applications combine results from different modalities in the inference phase to improve prediction accuracy. Existing MMML fusion strategies use static modality weight assignment, based on the intrinsic value of sensor modalities determined during the training phase. However, input data perturbations in practical scenarios affect the intrinsic value of modalities in the inference phase, lowering prediction accuracy, and draining computational and energy resources. In this work, we present DynaFuse, a framework for dynamic and adaptive fusion of MMML inference to set modality weights, considering run-time parameters of input data quality and sensor energy budgets. We determine the insightfulness of modalities by combining design-time intrinsic value with the run-time extrinsic value of different modalities to assign updated modality weights, catering to both accuracy requirements and energy conservation demands. The DynaFuse approach achieves up to 22% gain in prediction accuracy and an average energy savings of 34% on exemplary MMML applications of human activity recognition and stress monitoring in comparison with state-of-the-art static fusion approaches.