Determining optimal drug dosages in personalized medicine is a complex challenge due to the variability in patient responses and the dynamic nature of biomarker levels. Traditional methods often struggle to adapt to real-time changes and individual nuances, leading to suboptimal treatment outcomes and potential adverse effects. To address these issues, we propose FuRGAN, a novel framework that integrates fuzzy logic, recurrent neural networks (RNNs), and generative adversarial networks (GANs) to enhance personalized dosage recommendations. FuRGAN categorizes biomarkers into low, medium, and high levels using fuzzy logic, simplifying the initial decision-making process. RNNs are employed to analyze temporal patterns in patient data, enabling precise predictions of necessary dosage adjustments over time. GANs are utilized to generate and refine dosage recommendations through adversarial training, ensuring both realism and optimization. A key innovation of FuRGAN is its dynamic feedback loop, which allows the system to continuously adapt to real-time patient feedback and clinical outcomes. This continuous learning mechanism improves the framework's accuracy and efficacy, offering a more responsive and personalized approach to dosage personalization. By integrating these advanced technologies, FuRGAN aims to significantly enhance treatment outcomes and patient safety, providing a more effective solution to the challenges of personalized medicine.