Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks, and reliance on specialized infrastructure limit accessibility and scalability of standard-of-care right heart catheterization (RHC). Seismocardiography (SCG), a non-invasive technique which records subtle chest wall vibrations generated by cardiac mechanical activity, may offer a promising alternative for CO determination. To explore this potential, we developed and evaluated a deep learning model for estimating CO directly from SCG, electrocardiogram (ECG), and body mass index (BMI) in heart failure patients undergoing RHC. We trained a deep convolutional neural network for CO estimation using an open-access dataset comprising 73 heart failure patients with simultaneous RHC, SCG, and ECG recordings. Model performance was evaluated on 64 patients using pairwise nested leave-pair-out cross-validation. When estimating CO in patients with a reference output < 6 L/min, the deep learning model achieved a mean bias of −0.01 L/min with LoA from −0.88 to 0.87 L/min. When predicting cardiac index in patients with a reference index < 2.2 L/min/m ² , the model yielded a mean bias of 0.07 L/min/m ² with LoA from −0.35 to 0.48 L/min/m ² . This study demonstrates the feasibility of using deep learning in combination with wearable SCG sensors to non-invasively estimate CO. Model performance was particularly strong in low-output states. These findings highlight the potential of SCG-based monitoring to augment clinical decision-making in settings where invasive measurements are impractical or unavailable. Prospective multicenter validation is needed to confirm generalizability and assess clinical impact.