A sphericity value of 1 indicates a perfect sphere, with lower values signifying greater deviations. This metric is crucial in the settling velocity correlation to incorporate the impact of particle shape. Merely using sphericity is insufficient to describe the shape of non-spherical particles, as two particles with the same sphericity can have entirely different shapes and settling characteristics. The orientation of a particle also affects its drag coefficient and settling velocity. The drag force increases with the larger projected area of the particles perpendicular to the flow direction, which in turn affects the final settling velocity. However, sphericity alone cannot account for the effect of settling direction. Settling orientation also affects the particle settling process. And this phenomenon has been observed by many researchers [18; 20; 21; 22; 23; 24; 25; 26; 27; 28]. To account for the settling orientation effect, researchers have utilized various methods. For example, [PERSON] and [PERSON] [22] incorporated two additional sphericities in the crosswise and lengthwise directions to describe the settling orientation effect, but their study only focused on predicting the drag coefficient and did not investigate the settling velocity. Another approach is to use the settling area ratio (\(S\)), as utilized by [PERSON] et al. [29]. However, their model is only applicable to Newtonian fluids. Some scholars have employed numerical simulation methods to study the influence of settling orientation [25]. However, such methods are computationally expensive. The traditional methods for predicting settling characteristics have limitations, as they only consider either spherical or non-spherical particles in either Newtonian or non-Newtonian fluids. In petroleum engineering, where both types of fluids are common, the lack of formulas capable of predicting settling velocity for both particle shapes in both types of fluids is a significant drawback of the traditional approach. Furthermore, using different correlations for different fluids and particle shapes adds complexity to the process. For instance, non-Newtonian fluids require different correlations than Newtonian fluids. Artificial intelligence (AI) technology has gained significant attention in the petroleum engineering industry in recent years. Table 1 summarizes several studies that use AI technology to predict the drag coefficient and settling velocity of particles. [PERSON] et al. [30] developed an artificial neural network (ANN) to predict the terminal velocity of solid spheres, taking into account the properties of the sphere and the surrounding liquid. The ANN was trained on 88 sets of both Newtonian and non-Newtonian fluids data from published sources, accurately predicting terminal velocity of solid spheres falling through a wide range of power-law values (1.0 to 0.06). [PERSON] and [PERSON] [31] presented a novel machine learning approach, specifically using genetic programming, to predict noncohesive particle settling velocity. It utilizes a database of 985 published experimental measurements and outperforms common predictors in the literature. The study highlights the efficiency and accuracy of the machine learning approach in this field. [PERSON] et al. [32] compared BPNN and RBFNN models for predicting drag coefficients of non-spherical particles in gas-solid flow. The RBFNN model was found to be more effective, regardless of particle sphericity. The study provides valuable insights for fluidization studies. [PERSON] et al. [33] used ANNs to predict the terminal falling velocity of non-spherical particles in fluids. The ANN accurately predicted terminal velocities using 361 data points. This research offers a reliable method to predict particle behavior in fluid dynamics. [PERSON] et al. [34] developed an ANN model to predict drill cuttings' settling velocity in wellbores.