Internal Circulating Fluidized beds (ICFBs) are interested in various industries because of their higher thermal efficiency and high reaction rates. However, understanding the complex flow hydrodynamics inside an ICFB is challenging. Also, the experiments needed are expensive and time-consuming. Therefore this study aims to predict the solids circulation rate in an in-house ICFB (0.3 m internal diameter x 3.0 m height) using an empirical model and an Artificial Neural Network (ANN) technique. The solid circulation rates measured at different operating and design conditions using a high-speed video camera are utilized to develop the models mentioned earlier. A dimensionless approach and nonlinear regression models are adopted to derive the empirical model. The Analysis of Variance (ANOVA) technique calculates F-number and their corresponding probabilities (P-values). The ANN model is developed with four input variables: particle size, static bed height, gap height, and gas superficial velocity. Multi-layer Perception model (MLP) with the Feedforward Back Propagation learning rule is employed to build the ANN model. A single hidden layer with nine neurons predicted a close solid circulation rate with minimal error over the empirical model compared to experimental data. Further, the empirical and ANN model's predicting capability is tested against literature data. © 2022 Elsevier Ltd