The initial estimate of the critical voltage leading to the initiation of the electric arc carries great economic importance, because it positively affects the technical approach adopted in the analysis of electrical connections. This is especially true in many countries that tend towards a preventive approach by using of a large number of insulators. It has been found that empirical mathematical equations and models somewhat facilitate the estimation of the critical flashover voltage. However, these estimates can vary because of distinct constants associated with each type of insulator. The novelty of the proposed work is the development of new method by hybridization of two artificial intelligent techniques to make easy the estimation of critical flashover voltage value of insulators. The hybrid principle has been chosen, between the Particle swarm optimization (PSO) method, with a similar principle based approach, the Whale optimization Algorithm(WOA). The goal shifted towards employing artificial intelligence to find uniform arc constants that are compatible with all available insulators, and approach the experimental values achieved for each insulator. We randomly select initial values for the arc constants. Using the first method, we reach optimal values for these constants, and then we repeat the process several times to create a database of good solutions. We use this database as initial values for the second method to guide the solution, and we repeat the process each time. Taking into account the objective function on one hand and achieving the closest possible convergence with the experimental values on the other hand, we ultimately obtain two unified values for the arc constants that can be adopted in calculating the critical voltage for each type of insulator. After multiple iterations, we have achieved favorable results. The result obtained proved to be better than what was reached previously, and this confirms that the combination between the theories is a good solution that can be relied upon. The obtained values demonstrate good alignment with the experimental data, indicating their practical importance, especially in the context of technical study and economic considerations.