Accurate assessment of image quality in echocardiography is essential for both clinical interpretation and the performance of automated diagnostic tools. Rotational misalignment in apical four-chamber views is a common yet underexplored quality issue that can significantly impair anatomical interpretation and quantitative analysis. In this study, we propose a deep learning based framework for automated evaluation of rotational image quality in echocardiographic images. Leveraging a multi-image ranking annotation strategy, we trained a regression model on expert-annotated data. The model exhibited strong alignment with expert consensus, achieving Spearman’s correlation coefficients exceeding 0.88 across multiple validation sets. Comparative analysis demonstrated that model performance was on par with individual expert assessments. Additionally, a training set size analysis revealed performance plateauing beyond approximately 1,000 labelled samples, offering practical guidance for efficient annotation. These findings highlight the feasibility of scalable, objective, and clinically meaningful rotational quality assessment, with promising applications in real-time feedback, acquisition guidance, and automated quality control in echocardiographic workflows.