Abstarct: Drilling operations are among the most costly activities in the upstream petroleum industry, and they are of great importance. Several factors contribute to the success of this operation. Understanding the correlation between geomechanical and operational factors in drilling is crucial for enhancing drilling performance and reducing associated costs. Optimizing the penetration rate during drilling is crucial for the success of drilling operations. Factors such as the geomechanical properties, drilling fluid characteristics, hydraulics, and formation properties greatly influence the infiltration rate. In this study, the necessary information, including geological, petrophysical, and drilling data, was gathered and assessed in the target well located in a Persian Gulf field. Geomechanical parameters were estimated in the study well baxsed on petrophysical and rock mechanics data. Geolog software was used for one-dimensional geomechanical modeling to assess changes in pore pressure, stress, and wellbore stability conditions. Further, the drilling and geomechanical data from the well were consolidated to a single scale. Using this data, the correlation between geomechanical, petrophysical, and drilling categories and drilling penetration rate was analyzed. After the investigation, geomechanical modeling and drilling parameters were used to model the infiltration rate using MATLAB software. Modeling and optimizing the penetration rate were performed using two databaxses: one with only drilling data and another with drilling-geomechanical data. The correlation between geomechanical and petrophysical parameters and drilling penetration rate (ROP) was analyzed. We looked into 17 parameters, including RPM, WOB, UCS, FLR, Torque, PHIE, SPP, and HL from the drilling-geomechanical databaxse and 6 parameters from the drilling databaxse. The 6 parameters with the lowest RMSE value between the actual and predicted ROP data were RPM, WOB, FLR, Torque, SPP, and HL. To model the penetration rate, two methods, MLP and RF, were used. After comparing the parameters, the RF method was chosen as the final modeling method. The optimization was performed with the dual objectives of increasing the drilling speed and reducing the specific mechanical energy. Dupriest's equation was utilized to compute the specific mechanical energy. The MPSO algorithm was chosen for optimization, considering the characteristics of different algorithms for modeling the penetration rate. The average drilling speed controlled by the drilling databaxse was 14.1 meters per hour, while the geomechanical drilling databaxse achieved 15.5 meters per hour. The drilling time to reach a depth of 360 meters using the drilling databaxse was 2.65 hours, while the geomechanical drilling databaxse reduced this time by 4.9 hours, highlighting the significant impact of using geomechanical data.