The diagnosis of Autism Spectrum Disorder (ASD) remains a significant clinical challenge due to the subjective, and time-consuming of traditional diagnostic methods, often leading to delayed intervention. Although deep learning (DL) techniques have shown promise in improving diagnostic accuracy, existing approaches predominantly rely on individual pretrained models and have yet to achieve the precision necessary for dependable clinical use. While deep learning has shown promise, the systematic and comprehensive comparative analysis of diverse ensemble learning techniques for ASD detection, which have the potential to significantly enhance model performance by integrating multiple complementary models, remains underexplored. To address this gap, we propose a robust and efficient DL-based framework for ASD detection through facial image analysis. We fine-tuned multiple pretrained models – EfficientNetV2B0, EfficientNetB3, and MobileNet – on ASD-specific datasets. To develop a highly accurate, robust, and clinically applicable DL framework for ASD detection, we systematically implemented and compared several ensemble strategies, aiming to identify the most effective approach for reliable diagnostic support. Experimental evaluations on a publicly available facial image dataset demonstrate that the proposed ensemble methods outperform single-model baselines, achieving superior classification performance. Moreover, the ensemble framework exhibits competitive results relative to existing ASD detection methods, indicating improved generalizability and clinical applicability.