Janus nanofluids are formed by dispersing Janus nanoparticles (particles with asymmetrically modified surface properties) into a base fluid, which is reported to have a high thermal conductivity owing to excellent diffusion properties of the Janus particles. The thermal conductivity of nanofluids can be influenced by multiple parameters, including nanoparticle size, density, volume fraction, and others. It is a significant challenge to analyze the influence of these parameters on the thermal conductivity of nanofluids using traditional experimental and simulation approaches. Machine learning offers a powerful approach to handling nonlinear relationships and high-dimensional data. In this work, equilibrium molecular dynamics simulations are employed to calculate the thermal conductivity of Janus nanofluids containing silver, copper, or iron Janus nanoparticles. Based on the molecular dynamics simulation results, four machine learning models—Backpropagation Neural Network, Support Vector Regression, Group Method of Data Handling, and Random Forest—are applied to predict and analyze the thermal conductivity of Janus nanofluids. The input parameters for the machine learning models include nanoparticle size, nanofluid density, volume fraction, Janus nanoparticle asymmetry parameter, nanoparticle mass, and the thermal conductivity of the bulk nanoparticle material. It is found that the machine learning methods can effectively predict the thermal conductivity of Janus nanofluids. Among these machine learning models, Group Method of Data Handing has the most accurate predictions, with a root mean square error of 0.0058 W/(m·K) and a coefficient of determination of 0.9844. Feature importance analysis using SHAP for different input parameters indicates that the positive effects of each parameter are consistent with existing findings in open literature. This work validates the feasibility and robustness of the machine learning approach for predicting the thermal conductivity of Janus nanofluids and provides a valuable methodology for investigating and rapidly assessing Janus nanofluid thermal conductivity.