Metasurface-based solar absorbers are widely acknowledged in green energy applications. The traditional roadmap for designing meta-absorbers relies on hefty trial-and-error computations for reaching design goals. In contrast, emerging machine-learning (ML) techniques can make such designs faster and more efficient, while conserving computational resources. ML models, i.e. Decision Tree (DT) and Random Forest (RF) Regressors are demonstrated in this work to design a variety of meta-absorbers. The models have been trained to generate desired electromagnetic spectrum, shapes, and geometries of meta-atoms during forward and inverse configurations, respectively. The MSE of DT and RF are 8.61 × 10−10 and 1.56 × 10−2 for forward, while 4.42 × 10−2 and 1.35 × 10−1 for inverse models, respectively.