The biological functions of RNA arise from the interplay of sequence (1D), secondary structure (2D), and tertiary structure (3D). While existing machine learning models typically rely on sequence-based representations, recent studies suggest that integrating structural information can improve predictive performance, especially in low-data regimes. However, different representations have trade-offs—3D models are sensitive to noise, whereas sequence-based models are more robust to sequencing noise but lack structural insights. To address this, we introduce HARMONY, a framework that dynamically integrates 1D, 2D, and 3D representations, and seamlessly adapts to diverse real-world scenarios. Our experiments demonstrate that HARMONY consistently outperforms existing baselines across multiple RNA property prediction tasks on established benchmarks, offering a robust and generalizable approach to RNA modeling.