Porosity, permeability, and fluid saturations are considered critical petrophysical parameters for the reservoir because of their significant impact on the oil and gas reserve estimation as well as petroleum economics. For identifying reservoir properties, traditional methods rely on lab measurements, wireline logs, or empirical correlations between other parameters such as formation resistivity with hydrocarbon saturation, density with porosity, which are either expensive, time-consuming, or do not meet the accuracy requirements. All the foregoing limitations are overcome by the new horizon for integrating machine learning techniques as a new way for predicting these properties. Therefore, this work will develop a new prediction model by combining advanced data analytics with visualization approaches for pre-processing the logs collected in the Sultanate of Oman's Gharif Reservoir. The relationship between the output and input variables was investigated using descriptive statistics, then the Extreme Gradient Boosting (XGBoost) regression model was applied to predict reservoir porosity, water saturation, and permeability. Then, to minimize the dimensionality of the input features, various feature selection techniques were evaluated, including SelectKBest, Recursive Feature Elimination, Random Forest, and Principal Component Analysis. To improve the accuracy of the results, a novel custom ensemble model of Random Forest and Recursive Feature Elimination with an enhanced feature engineering technique was presented. The proposed unique ensemble model appears to outperform the classic XGBoost, revealing the immense potential of ensemble modeling for reservoir characterization. The new custom ensemble model reduced the characteristics of the high-dimensional dataset while also achieving low MAE and RMSE prediction errors of 0.0037 & 0.023 for permeability and saturation respectively