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Heat transfer enhancement using curved ribs of different cross sections, viz., square, rectangular, triangular, and circular, is a crucial study for designing heat-exchanging devices for various applications, and their thermohydraulic performance prediction using machine learning technique is a vital part of the modern world. An experimental study on using curved ribs suitable for heat transfer enhancement for the circular tube is presented for turbulent airflow with Reynolds numbers varying from 10,000 to 50,000. The machine learning methodology is used to predict the thermohydraulic performance assessment of curved ribs. The square cross-sectioned curved ribs produce the highest performance factor <i>R3</i> of 1.5 to 2.65 to the equivalent Reynolds number <i>Rec</i> value of 20,000. It is observed that most of the curved rib configurations show a performance ratio <i>R3</i> maximum and are suitable at a low Reynolds number value. At moderate and high Reynolds number values, the performance factor values decrease due to a rise in the pressure drop values for a few curved rib configurations. An artificial neural network (ANN) model predicts with an accuracy of 95% with the present study experimental values for the heat transfer performance indicators like average heat transfer enhancement <i>Nua/Nus</i>, average heat transfer enhancement <i>fa/fs</i>, and performance ratio <i>R3</i>, i.e., <i>Nua/Nuc</i>.