Delay Differential Equations (DDEs) are differential equations with time delays, which are used in various life sciences disciplines like population dynamics, epidemiology, immunology and physiology. These models are used to analyze and predict phenomena, such as the duration of latent processes like the life cycle, infection, and immune response. The dynamics of a system at a given moment in time are influenced by its previous history or memory, increasing the complexity of the system and improving the dynamics of a differential model. Fractional models of DDEs have been used to explore various phenomena, such as brain networking, population dynamics, and physiology. This study investigates the numerical approximations of the Nonlinear Fractional Pantograph Delay Differential Equation (NFPDDE) using the Fractional Novel Analytical Scheme (FNAS) and optimization procedures based on the Genetic Algorithm (GA), referred as Fractional Novel Analytical Genetic Algorithm (FNAGA). The FNAGA is used to optimize an error-based fitness function constructed through fractional delay differential equations. The Conformable fractional derivative T η is taken into consideration. To implement the proposed methodology, an error analysis is conducted. The solution behavior of NFPDDEs is also shown graphically at different values of η. The findings of the FNAGA are contrasted with those of the FNAS, indicating that the newly developed algorithm exhibits rapid convergence, produces precise solutions, and demonstrates enhanced accuracy. The effectiveness of the proposed method in achieving the synchronization objective is demonstrated through simulations and can be easily applied to various fractional models.