Coal and gas outburst is one of the highly destructive geological hazards in coal mining operations, and efficient outburst early warning is crucial for ensuring the safe production of mining areas. To resolve traditional Stacking models′ issues of inefficient hyperparameter optimization, local optima traps, and manual base learner selection, this paper develops an improved Stacking early warning model with modified artificial rabbit optimization （ARO） and differential evolution （DE） algorithms. The AMSARO algorithm—incorporating an elite memory pool and a multi-strategy adaptive selection mechanism—is applied for parameter optimization. The DE algorithm dynamically optimizes weights to realize base learner selection while performing feature selection synchronously. Combining these two algorithms with the Stacking ensemble method, the DE-AMSARO-Stacking coal and gas outburst early warning model is constructed. Eight different benchmark functions are adopted for testing, and experimental results show that the AMSARO algorithm achieves faster convergence speed and higher optimization accuracy compared with the ARO algorithm and its improved variants. Experiments are conducted on a dataset of 500 samples expanded from 50 sets of original data collected from a coal mine in Shanxi Province. The results indicate that the DE-AMSARO-Stacking model outperforms single models and comparative models based on different optimization algorithms in prediction performance. This research provides a more efficient approach for coal and gas outburst early warning.