Echocardiography is a mainstay of cardiovascular care offering non-invasive, low-cost, increasingly portable technology to characterize cardiac structure and function. Artificial intelligence (AI) has shown promise in automating aspects of medical image interpretation, but its applications in echocardiography have been limited to single views and isolated pathologies. To bridge this gap, we present PanEcho, a view-agnostic, multi-task deep learning model capable of simultaneously performing 39 diagnostic inference tasks from multi-view echocardiography. PanEcho was trained on >1 million echocardiographic videos with broad external validation across an internal temporally distinct and two external geographically distinct sets. It achieved a median area under the receiver operating characteristic curve (AUC) of 0.91 across 18 diverse classification tasks and normalized mean absolute error (MAE) of 0.13 across 21 measurement tasks spanning chamber size and function, vascular dimensions, a