Advances in high-performance computing (HPC) enable the simulation of complex, multiscale flow phenomena relevant to aerospace with unprecedented accuracy, generating vast high-fidelity datasets. In parallel, scientific machine learning (SML) is rapidly transforming the physical sciences. When combined, SML and HPC promise a step change in predictive capabilities and computational efficiency for CFD, with potential impact on decarbonizing aviation and other carbon-intensive sectors. Yet, key challenges remain: selecting informative training data, generalizing to out-of-distribution conditions, and quantifying predictive uncertainty. In this talk, Paola will explore how the synergy between HPC and SML can be harnessed to accelerate sustainable aviation, and highlight current bottlenecks and research directions toward trustworthy, scalable ML-enhanced CFD.