This study presents a new model to predict the static formation temperature at multiple oil fields using multiple machine learning algorithms. Results are compared with the real obtained temperatures from two wells. The model developed in this study uses Python and Matlab to predict static formation temperature according to several machine learning algorithms including artificial neural networks, fuzzy logic, k-nearest neighbors, and random forest algorithms. Also, we used well-known analytical methods called line-source-Horner and spherical & radial heat-flow (SRF). The following are the key findings: Using well-known analytical methods, we estimated formation hole temperatures, our results demonstrated that The Horner approach overestimates the static temperature of the formation., but the SRF method gives more accurate temperatures. Using Python and MATLAB programming languages, based on machine learning models, we developed a novel model for forecasting static formation temperature., examined the modeling data and outcomes, and found that Random Forest (RF), Fuzzy logic (FL), and K-nearest neighbors (KNN) outperformed Artificial Neural Network (ANN). Therefore, the performance of the proposed model for estimating the static well temperature achieved an error percentage of AAPE 0.003% and R2 is 0.99%. The findings generated by the unique new model computation and the measured test data are substantially matched when compared to computed data and observed temperature data. The novelty of this newly developed AI model is that it will serve as a practical and inexpensive tool for SFT determination in geothermal and petroleum wells. Keywords: Static Formation temperature; artificial intelligence; predicting the static formation temperature at multiple oil fields; Honor method; SRF method