The performance of Remaining Useful Life (RUL) prediction models is often limited by data scarcity, especially in safety-critical systems like aircraft engines where failure data is rare. To address this challenge, we propose the Super-SpaceTime GAN, a framework for generating synthetic condition monitoring (CM) data to enhance RUL predictions. The framework incorporates dual-conditioning on operating conditions (OCs) and RUL labels, an autoencoder-based latent space for denoising, and a supervised loss function to align synthetic data with real degradation trajectories. Evaluated on the CMAPSS FD004 dataset, the SuperSpaceTime GAN generates synthetic data that closely mimic real distributions, as verified using JensenShannon Distance, Principal Component Analysis, t-distributed Stochastic Neighbor Embedding, and a novel autoencoder-based health monitoring metric. The framework demonstrates improvement in prognostic performance in limited training data scenarios, with gains persisting even in data-rich settings. These findings highlight the potential of the Super-SpaceTime GAN to improve RUL predictions by addressing data scarcity, making it a valuable tool for Prognostics and Health Management (PHM).