Adverse events (AEs) can significantly impact patient health. The implicating drugs have to either stopped or decrease the dose. So, making accurate causality assessment crucial. Existing scales like NS and WHO have limitations in assessing and interpreting the AEs. So we have developed a novel causality assessment scale (SNG scale) for AE causality assessment. We are validating the scale in the patients with adverse events. The study aims to demonstrate the SNG algorithm’s concordance with physician evaluations and its superiority over existing methods in various scenarios. In addition, we will be comparing its performance with the Naranjo scale (NS) and WHO scale and with the clinician’s assessment. The study is a multi-centric, cross-sectional design, enrolling patients who have experienced adverse events. It involves an interview-based approach to collect data, and the causality is assessed using the SNG, NS, and WHO algorithms. By enhancing causality assessment methods, the study seeks to improve patient safety and treatment efficacy. The SNG algorithm’s validation could lead to its broader adoption in clinical settings.