During the exploration phase, understanding the reservoir is a demanding task due to geological complexities and limited data availability. Advanced techniques are used by geoscientists to interpret available data, such as well logs and seismic data, for gaining insights about porosity, permeability, lithology, and hydrocarbon saturation of the reservoir. Seismic inversion is one of these key techniques that coverts seismic data into elastic properties like P-impedance and S-impedance. This technique performs excellently given the seismic data is high-quality and minimally corrupted by noise and multiples. However, its efficiency deteriorates with poor quality seismic data. Machine Learning serves as a viable alternative in these situations, offering the potential to transform seismic data into elastic and physical properties, showing satisfactory results and performance in many instances. The Gharif Formation is a significant oil and gas reservoir producer in Oman, primarily comprising sand and shale with a carbonate layer easily identifiable in seismic data. However, differentiating between sand and shale layers within the Gharif Formation proves difficult due to its depth and low seismic resolution within its range. Furthermore, distinct differences in physical and elastic properties make it especially challenging to visualize channel extensions and segregate sand from shale in the Gharif Formation. This project aims to leverage Machine Learning and Deep Learning capabilities to convert seismic data into elastic properties, P-impedance, S-impedance, and Vp/Vs, facilitating the differentiation of sand from shale in the Gharif Formation. Several algorithms were tested to achieve optimal results, including eight key algorithms: shallow algorithms like Linear Regression, Ridge Regression, Lasso Regression, Support Vector Machine Regressor, Rain Forest Regressor and Multilayer Perceptron, and Deep algorithms such as Convolutional Neural Networks and Deep Neural Networks. The study was divided into three stages. The first stage involved using various algorithms to transform synthetic seismic data generated from wells into Pimpedance. The shallow algorithms' results weren't as effective as expected, achieving a correlation of about 70%. Conversely, the deep algorithms demonstrated excellent results, with DNN, CNN, and the shallow algorithm MLP achieving 89.65%, 89.09%, and 76.05% of accuracy respectively. In the second stage, MLP, DNN, and CNN algorithms were compared regarding their performance in predicting P-impedance from actual 3D full-stack seismic data, with MLP outperforming the rest. The project's final stage involved using substacks of seismic data (near, near-mid, mid and far) to predict P-impedance, Simpedance, and velocity ratio. The AI-based inversion results were validated using seven blind wells, showing a similar performance to the science-based AVO inversion. Subsequently, the AI-based inversion outputs were used to calculate sand and shale probabilities using the rock fluid index (RFI). The probability cube enabled the extraction of several maps below the upper Gharif horizon to highlight the channels.