The college Faculty of science at the University of Basrah organized a panel discussion on predicting the permeability of reservoir rocks using conventional well Records, Nuclear Magnetic Resonance (NMR) using machine learning, deep learning and Python Programming Language: case studies from fatati rock reservoirs in southern Iraq. The seminar aimed at calculating permeability based on machine learning and deep learning, comparing the results obtained with each other and with the results derived from the rock core and the magnetic resonance sensor, and making sure the accuracy of the data results and their conformity to reality. The seminar, which was attended by master's graduate student Ali Zuhair Abdullah, included permeability, a fundamental property of rocks that determines the ability of fluids such as oil, gas and water to move through the pores of the rock. Machine learning techniques have proven effective in estimating permeability using rock and sensor data. These techniques rely on training models such as Linear Regression, SVM, Random Forest, and Gradient Boosting to analyze patterns and predict permeability with high accuracy. Machine learning is a promising alternative to traditional methods, providing a solution that combines accuracy, speed and low cost, enhancing the efficiency of oil studies.