NASA’s Solar Dynamics Observatory (SDO) continually captures extensive, high-quality, multi-instrument solar data, turning heliophysics into a data-intensive discipline. This vast observational record offers a unique opportunity to leverage machine learning (ML) techniques to tackle persistent challenges in solar and heliospheric physics. However, seamless application of SDO data requires specialized preprocessing to homogenize observations from multiple instruments. To fully exploit the highest-quality data available from SDO, we introduce SuryaBench. The dataset includes preprocessed ML-ready imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) instruments, spanning a solar cycle from May 2010 to Dec 2024. We also provide auxiliary datasets complementing the core SDO dataset. These provide benchmark applications such as active region segmentation, active region emergence forecasting, coronal field extrapolation, solar flare prediction, and solar wind speed estimation. By establishing a unified, standardized data collection, this dataset aims to facilitate benchmarking, enhance reproducibility, and accelerate the development of AI-driven models for critical space weather prediction tasks.