Quantitative formulation between conventional well logs and Poisson's ratio, the most critical geomechanical property of reservoir rocks, could be a potent tool for planning and post analysis of wellbore operations. Direct estimation of Poisson's ratio from conventional well logs makes the problem too complicated. Therefore, the present study proposes an improved multi-step strategy for making a quantitative formulation between conventional well logs and Poisson's ratio. In the first stage, shear wave slowness was predicted from conventional well logs using a radial basis neural network, Sugeno fuzzy inference system, neuro-fuzzy algorithm, and simple averaging method. Consequently, the Poisson's ratio was computed from the results of each expert, independently. Eventually, a committee machine with intelligent systems was constructed by virtue of a hybrid genetic algorithm-pattern search technique. The values of Poisson's ratio, derived from the results of a radial basis neural network, Sugeno fuzzy inference system, neuro-fuzzy algorithm, and simple averaging method, were used as inputs of the committee machine with intelligent systems. The proposed committee machine with intelligent systems combines the results of aforementioned experts for overall estimation of Poisson's ratio from conventional well log data. It assigns a weight factor to each expert, indicating its contribution in overall prediction. The proposed methodology was applied in Asmari formation, which is the major carbonate reservoir rock of Iran. A group of 1,582 data points were used to establish the intelligent model, and a group of 600 data points were employed to assess the reliability of the proposed model. The results show that the committee machine with intelligent systems method performs better than individual intelligent systems, which perform alone.