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The airfoil fin printed circuit heat exchanger (PCHE) has great potential for deployment in advanced Brayton cycles with supercritical CO<inf>2</inf>-based mixtures as working fluid. However, accurately predicting the complex flow and heat transfer behavior in the PCHEs remains challenging. This study employs two prominent machine learning (ML) methods – Artificial Neural Networks (ANN) and Extreme Gradient Boosting (XGBoost) – to predict the thermal-hydraulic performance (i.e., Nusselt number Nu and Fanning friction factor f) of supercritical CO<inf>2</inf>-based mixtures in airfoil fin channels. Beyond conventional predictive modeling, an innovative application of SHAP (SHapley Additive exPlanations) analysis is introduced to provide novel physical understanding of model outputs. The results demonstrate that both ANN and XGBoost exhibit excellent prediction performance, significantly outperforming conventional correlations, with R2 exceeding 0.99 for Nu and approaching 0.95 for f as well as small root mean square error (RMSE) and weighted mean absolute percentage error (wMAPE). The models also effectively capture the structurally-induced fluctuations of Nu and f along the flow direction. The feature selection based on the Spearman correlation coefficient method yields a more compact feature space without compromising predictive capability. SHAP analysis reveals a consistent and dominant influence of heat flux (q) and Reynolds number (Re) on the predictions for two targets, with q primarily affecting Nu, while Re and the densities ratio (ρ<inf>b</inf>/ρ<inf>w</inf>) are crucial for f. Notably, a previously overlooked positive impact of the buoyancy effect on improving hydraulic performance is identified in this study. These findings demonstrate significant potential of explainable ML models in predicting the complex supercritical thermal-hydraulic performance, promoting reliable design and optimization of novel heat exchangers in supercritical Brayton cycles. © 2025 Elsevier B.V., All rights reserved.