Purpose: Quantitative analysis and dose determination in ¹⁸F-FDG PET imaging are commonly based on total body weight; however, such approaches do not adequately account for individual differences in body composition, including fat and lean tissue proportions. This study aimed to propose and validate a radiopharmaceutical dose correction model based on organ volume derived from CT images, reflecting metabolically active tissue. Materials and Methods: Parenchymal organ volume data (liver, kidneys, and spleen) from 790 adults were extracted from the TotalSegmentator open-source dataset. A weighted sum model was established based on a standard adult (65 kg), incorporating a sensitivity (damping) factor and a clamping technique (±30%) to prevent excessive dose variation. Model optimization was performed using 5-fold cross-validation, and clinical feasibility was evaluated using CT data from nine patients. Results: At the optimal sensitivity parameter (S=0.5), more than 95% of the simulated cohort fell within the clinically recommended dose range (148~208 MBq), with a mean calculated dose of 178.00±16.65 MBq. External validation demonstrated that, despite substantial variations in liver volume (730~2,965 cm³), the predicted doses consistently remained within the safety range, effectively preventing both over-administration and under-administration of radiopharmaceuticals. Conclusion: The proposed organ volume-based model enables stable and reasonable dose estimation without relying on total body weight information. This approach may serve as a practical and safe alternative for personalized dose optimization and provides a foundational framework for future AI-based automated prescription systems.