We present a method for reconstructing the gold-standard electrocardiogram (ECG) from the more easily obtainable phonocardiogram (PCG). Both ECG and PCG are commonly employed in the primary diagnosis of various cardiovascular diseases (CVD). By leveraging a Bidirectional Long Short-Term Memory (BiLSTM) network capturing bidirectional temporal dependencies in ECG-PCG pairs, our approach effectively reconstructs key morphological features of the ECG, evidenced by strong performance metrics in healthy subjects. Although handling diverse pathological ECG patterns remains challenging, the model successfully reconstructs essential ECG structures in most cases; noise in PCG signal was found to potentially compromise reconstruction accuracy. Importantly, the combination of ECG and PCG data offers a more complementary perspective, aiding informed decision-making for a variety of CVDs. This transformation opens new clinical avenues offering a simple and convenient approach for obtaining critical ECG data with a smartphone’s onboard microphone, significantly enhancing diagnostic capabilities and patient care ‘in the field’.