## 1 Introduction
The effectiveness of these nanoparticles was assessed through transmission electron microscopy (TEM) images. The ANN model included one input layer, one hidden layer, and one output layer. Its performance was superior to that of Multi-Linear Regression (MLR) and Random Forest (RF) regression algorithms in terms of statistical accuracy. Based on their findings, the developed ANN model was a highly accurate and non-destructive alternative approach to traditional UCS tests, offering cost and time-saving advantages to the petroleum industry [15]. [PERSON] et al. [16] carried out a study to investigate the geomechanical parameters used for field planning and development, specifically focusing on the tensile and UCS values of rock. Given the time-consuming nature of laboratory measurements, the researchers employed non-destructive techniques to expedite and enhance the reliability of predictions. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Formula** & **Region** & **Remarks** \\ \hline UCS \(=143.8\times\exp(-6.95\varnothing)\) & Middle East & \(0.05<\phi<0.2\) and \(30<\text{UCS}<150\) MPa \\ UCS \(=135.9\times\exp(-4.8\varnothing)\) & - & \(0<\phi<0.2\) and \(10<\text{UCS}<300\) MPa \\ UCS \(=276(1-3\varnothing)^{2}\) & Korobcheyev & - \\  & deposit, Russia & - \\ UCS \(=\left(\frac{76\Delta t}{\Delta t}\right)^{1.82}/145\) & - & - \\ UCS \(=10^{1.044+\frac{10\Delta t}{2\Delta t}}/145\) & - & - \\ UCS \(=7600\times\exp(-0.064\Delta t)\) & Middle East & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Empirical relationships between UCS and petro-physics logs in carbonates. They used an ANN to predict Ts and UCS based on drilling data obtained from two Middle Eastern fields. The ANN was highly accurate in predicting both parameters during the training phase and was effective in predicting them during the testing and validation phases with an average AAPE of 0.59% [16]. [PERSON] et al. [17] investigated the use of machine learning to predict the UCS and tensile strength (T0) of carbonate rocks from well-log data. They utilized RF and decision tree (DT) algorithms on data from a Middle Eastern reservoir, identifying gamma ray, compressional time, and Bulk Density as key predictive factors. The study found both models to be highly accurate, with RF slightly outperforming DT. Specifically, RF achieved a correlation coefficient (R) of 0.97 and an absolute average percentage error (AAPE) of 0.65% for UCS prediction, and an R of 0.99 and AAPE of 0.28% for T0. These results suggest that machine learning offers a reliable and efficient method for estimating rock strength parameters, though further research is needed for other geological formations [17]. This study aims to evaluate the effectiveness of various machine learning models in predicting the UCS of rocks within the context of oil and gas wells, which is a key factor in maintaining wellbore stability and optimizing drilling operations. By comparing Linear Regression, ensemble methods (such as Random Forest, Gradient Boosting, XGBoost, and LightGBM), support vector machine-based regression (SVM-SVR), and multilayer perceptron artificial neural network (MLP-ANN) models, this research seeks to identify the most reliable and accurate approaches. This study not only highlights the importance of selecting suitable machine learning models for geotechnical applications but also advances the field by providing valuable insights into rock behaviour under drilling conditions. These insights pave the way for further research, ultimately improving the understanding of geomechanical properties and their impact on drilling operations.