Laser atmospheric propagation is influenced by combined effects including turbulence, thermal blooming, atmospheric inhomogeneity, and other perturbations. Key beam quality metrics—such as target spot expansion ratio, spot radius growth, centroid displacement, and encircled energy ratio—quantify beam distortion and attenuation during atmospheric propagation, enabling systematic evaluation of laser propagation performance. Existing models fall into three categories: wave-optics models, empirical scaling-law models, and statistical analysis models. Wave-optics models provide high precision but suffer from prohibitive computational complexity for real-time applications. Empirical models simplify calculations but fail under extreme conditions (e.g., strong turbulence or thermal blooming). Statistical models enable rapid predictions but produce ensemble-averaged results insensitive to transient/local variations, require stringent data quality, and lack interpretability. This study introduces a Lasso regression-based framework to address these limitations, achieving real-time capability, high accuracy, and interpretability for laser atmospheric propagation assessment.Figure 1 outlines the Lasso regression modeling workflow (Fig.1). Simulation data were generated using a four-dimensional high-energy laser atmospheric propagation and adaptive optics compensation code developed by the Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (Tab.1). The code implements a multi-phase-screen propagation model, with datasets comprising laser parameters (wavelength, power), atmospheric parameters (turbulence strength, thermal blooming distortion), and beam quality metrics. Lasso regression with L1 regularization was applied to model beam quality degradation mechanisms, automatically selecting dominant features from high-dimensional data while suppressing noise. Hyperparameters (regularization strength, convergence tolerance) were optimized via grid search (Fig.2, Tab.2).The Lasso regression-based model resolves critical limitations of conventional methods in real-time performance, accuracy, and feature interpretability (Tab.3). Leveraging Lasso’s feature selection mechanism, the model achieves precise predictions of beam quality metrics while maintaining computational efficiency and interpretability. Compared to traditional statistical models, it delivers superior prediction accuracy and faster computation, fulfilling real-time evaluation requirements in practical engineering scenarios. Simulation analyses demonstrate robust performance under complex atmospheric conditions, including strong turbulence and thermal blooming (Fig.3-Fig.7).The proposed Lasso regression model enables rapid, accurate evaluation of laser atmospheric propagation under extreme conditions, addressing the trade-off between computational cost and physical fidelity. Its embedded feature selection mechanism aligns with laser propagation physics (e.g., turbulence-driven beam wander vs. thermal blooming-induced defocus), enhancing interpretability for field deployment. Future efforts will extend the framework to multi-wavelength/pulse regimes and hybrid machine learning architectures (e.g., physics-informed neural networks) for improved generalizability.