Generally, these studies have relied on data obtained from laboratory studies of DF samples with various concentrations and additives of nanoparticles, which may not precisely reflect the actual drilling fluid compositions during the drilling of a specific well. [PERSON] and [PERSON] [10] compiled a 1800-point dataset from WBDFs and OBDFs and applied random forest (RF), XGBoost (XGB), support vector machine (SVM), and multilayer perceptron (MLP) models to study correlations between fluid properties and the FV. Their RF model achieved the best prediction performance (R\({}^{2}\) = 0.86, mean absolute percentage error (MAPE) = 22.56%). Although their proposed model generated low prediction errors, its applicability is limited as rheological parameters were used as inputs, which are too time-consuming to obtain frequently during drilling operations. [PERSON] et al. [26] applied HML, LSSVM, a multilayer extreme learning machine (MELM) improved by a cuckoo optimization algorithm (COA), and a genetic algorithm (GA) to predict the FV, the PV, and the YP using 1160 records. The inputs used were the fluid density (FD), the solid percentage (S%), and the Marsh funnel viscosity (MFV). The MELM-COA models achieved the best prediction performance with RMSE values of 0.6357, 0.6086, and 0.6796 for the FV, the PV, and the YP, respectively. However, S% is determined daily, and its measurement takes a relatively long time (2-3 h/experiment), limiting that model's use for frequent FV monitoring in a semi-real-time manner. Consequently, the existing studies have provided results that demonstrate the capability of using ML, deep learning (DL), and HML models to predict the FV in DFs. However, these previous studies generally rely on published data and/or laboratory-generated fluids, which may not accurately reflect the drilling conditions at a specific time. Moreover, the existing FV prediction models typically have unreliable prediction performance and require new techniques to further leverage comprehensive real-time data. This study aims to address these limitations by utilizing semi-real-time data and advanced HML modeling to improve the FV prediction performance. The FV can be predicted more frequently by relying on just two input variables, FD and MFV, commonly measured at the well-site every 15-20 min (2 to 3 measurements/h recorded continuously while drilling) [11; 27]. These parameters are highly correlated with the FV, as drilling-fluid additives tend to affect DF density and viscosity. ML maps FD-MFV to typical FV outcomes, enabling FV approximation without direct measurement. This information facilitates proactive responses such as adding filtration control additives to modify DF compositions before drilling problems occur. Similarly, MFV provides a practical measure of fluid rheology, indirectly reflecting changes in fluid properties that materialize during drilling [27; 28; 29]. This novel approach improves upon previous studies, which inadequately predict the FV by relying on measurements taken only about once per day, making them unable to identify rapid downhole changes during drilling operations. The proposed model fulfills the need for a reliable prediction tool that can predict the FV using semi-real-time DF data at short intervals (i.e., several times per hour). This study, therefore, addresses the research gap by developing a novel HML model to reliably predict the FV based on only the two most readily experimentally measured parameters--FD and MFV. The dataset evaluated includes 1260 records from seventeen wells drilled in two Iranian oil and gas fields using various WBDFs. Two ML models are developed: radial basis function neural network (RBFNN) and multiple extreme learning machine (MELM). These ML models are hybridized with the growth optimizer (GO) to fine-tune the models' hyperparameters.