Background <p>Lens stiffness plays a central role in cataract surgery planning, especially for phacoemulsification. Current assessments rely on intraoperative judgment, which limits personalized surgical strategies. This pilot study aimed to develop the first machine learning model using extracapsular cataract extraction–derived ground-truth data to predict the stiffness of cataractous human lenses using routinely available demographic and clinical features.</p> Methods <p>Fifty human lens nuclei were obtained following extracapsular cataract extraction. Predictive features included age, gender, cataract grade, lens diameter, diabetes status, and aspirin use. Each lens was exposed to three compressive loads of 0.2 N, and deformation was measured using ultrasound imaging. Young’s modulus was calculated for each trial. Data preprocessing, exploratory analysis, and model training were performed. Several supervised regression algorithms were developed and compared in terms of predictive accuracy and generalization.</p> Results <p>Among the implemented 10 regression models, the Ridge model demonstrated the highest predictive accuracy. Feature importance analysis identified cataract grade, lens diameter, age, and diabetes status as the strongest predictors of stiffness. Aspirin usage and gender showed relatively weaker contributions to model performance.</p> Conclusions <p>Supervised machine learning appears feasible for estimating lens stiffness from routine demographic and clinical data. Moreover, given the small, nonrepresentative ECCE-based sample, these findings should be regarded as proof-of-concept and exploratory. Validation in substantially larger, independent phacoemulsification-based cohorts is required before any clinical application can be considered.</p>