The left atrial appendage (LAA) is a major site for thrombosis, responsible for 90% of strokes in patients with atrial fibrillation (AF). Variations in LAA morphology can significantly impact blood stasis and thrombosis risk, making an accurate assessment of its shape crucial for stroke prevention. In this work, we present a fully automated computational pipeline that integrates deep learning (DL)-based segmentation, computational mesh generation, statistical shape analysis, clustering, and computational fluid dynamics (CFD) simulations to analyze patient-specific LAA morphology and flow dynamics. Our approach achieves accurate 3D segmentation of the left atrium (LA), pulmonary veins, and LAA from CTA scans, with an average Dice score of 0.93. The LAA is then extracted at the ostium following clinical guidelines and aligned using iterative closest-point registration to establish a standardized template for shape analysis. To categorize different LAA morphologies, principal component analysis (PCA) is used, followed by clustering techniques. Our pipeline is fully automated, enabling the transition from CT cardiac images to CFD simulations with a 93% success rate across 812 patients - the largest cohort of LA simulated patients in the literature. For each patient, two CFD simulations are conducted to evaluate the effect of boundary conditions under both sinus rhythm and AF. The resulting hemodynamic insights can aid in identifying high-risk LAA morphologies, potentially guiding personalized stroke prevention strategies and device interventions.