Predicting interplanetary coronal mass ejections (ICMEs) remains challenging due to limited datasets, the complexity of some approaches based on machine learning and the continued reliance on manual event validation. We introduce SWAAT (Space Weather Automated Analysis Tool), a framework that integrates automated validation with synthetic data-driven prediction to improve ICME transit time (TT) forecasts based on propagation velocity (Vp). The system comprises two main modules: (i) The Automated Data Acquisition and Analysis (ADAA), which automates ICME candidate validation using GOES X-ray and SDO imagery, and (ii) The Generative-Evolutionary Synthetic Data (GESD), which employs evolutionary algorithms to generate optimized synthetic datasets for model training. By combining automatic event validation with quality synthetic data, SWAAT mitigates the limitations imposed by scarce datasets thanks to the contribution of synthetic data, improving the accuracy of space weather forecasting.
