ContinualFlow is a novel framework designed for targeted unlearning in generative models, utilizing Flow Matching and an energy-based reweighting loss to effectively remove undesired data distribution regions without extensive retraining. The method demonstrates its effectiveness through various experiments and provides visualizations and quantitative evaluations to support its claims.