The model considers various shapes of cuttings and fluid properties, providing a more accurate and innovative approach to estimating settling velocity under real-world drilling conditions. [PERSON] et al. [35] compared three machine learning models to estimate sediment settling velocity. The decision tree (DT) model outperformed other models and traditional methods in predicting velocity for sand and gravel. Advanced modeling techniques can further improve estimation methods. [PERSON] et al. [36] developed an empirical correlation to predict the settling velocity of solids in non-Newtonian liquids using experimental methods and ANN modeling. The study confirmed the effectiveness of ANN techniques in predicting settling velocity more accurately than previous methods. [PERSON] et al. [37] used machine learning to predict settling velocity and found support vector regression with a polynomial kernel to be the most effective method. Their model was validated through rigorous statistical methods on 967 fluid samples, highlighting the potential of AI in fluid dynamics. In follow-up research, they used a large dataset to evaluate machine learning models for predicting the settling velocity of particles in Newtonian fluids, offering a more accurate and reliable method using advanced AI techniques. [PERSON] et al. [28] developed ANN models to predict proppant settling velocity and orientation in vertical fractures. Based on 588 experiments, they analyzed the impact of proppant shape, fluid properties, and fracture wall effects on the settling characteristics. The insights we gained can improve hydraulic fracturing performance. [PERSON] et al. [38] used AI to predict the settling velocity of spherical and non-spherical particles. Their detailed dataset improved accuracy and reliability. This study applied AI in a field that typically relies on empirical and experimental methods. [PERSON] [39] developed seven equations using artificial neural network (ANN) methodology to predict the settling velocity of sediment particles. Data were sourced from digitized charts by the U.S. Interagency Committee on Water Resources, and the equations were compared with existing ones for validation. This presents a new approach in sedimentology for predicting settling velocities. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **AI Model** & \begin{tabular}{c} **Number of** \\ **Data Points** \\ \end{tabular} & **Fluid** & **Particle** & \begin{tabular}{c} **Input** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **Predicted** \\ **Parameter** \\ \end{tabular} \\ \hline [PERSON] et al. [30] & ANN & \begin{tabular}{c} 88 (63 train, 25 \\ test) \\ \end{tabular} & [PERSON], PL & Spherical & \(\rho_{f},\rho_{p},d_{p},K,n\) & \(U_{p}\) \\ \hline [PERSON] and & GP & \
\begin{tabular}{c} 985 (40 train, \\ 472 validation, \\ 473 test) \\ \end{tabular} & Newt & Non-spherical & \(d_{p},\
u,R\) & \(U_{p}\) \\ \hline [PERSON] et al. [32] & BPNN, RBFNN & — & Newt & \begin{tabular}{c} Spherical, \\ Non-spherical \\ \end{tabular} & — & \(C_{D}\) \\ \hline [PERSON] et al. [34] & ANN & \begin{tabular}{c} 336 (236 train, \\ 50 test, 50 validation) \\ \end{tabular} & Newt & \begin{tabular}{c} Spherical, \\ Non-spherical \\ \end{tabular} & \(\Phi,\rho_{p},d_{p},\mu,\rho_{f}\) & \(U_{p}\) \\ \hline [PERSON] et al. [35] & FFNN, DL, DT & \
\begin{tabular}{c} 756 (529 train, \\ 227 test) \\ \end{tabular} & Newt & Non-spherical & \(d_{p},\
u,R\) & \(U_{p}\) \\ \hline [PERSON] et al. [36] & ANN &
\begin{tabular}{c} 54 (38 train, 11 \\ validation, 5 test) \\ \end{tabular} & PL & Non-spherical & \(n,K,\rho_{f},\phi,d_{p}\), \(d_{t}\) & \(U_{p}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies on prediction of drag coefficient and settling velocity of particles using AI technology. Implementing AI, particularly ANN, in predicting settling velocity is motivated by several factors. (1) ANN can handle a broader range of variables and interactions, making them suitable for complex systems.