Accurate forecasting of pedestrian counts in the Central Business District of Melbourne during extreme scenarios such as the COVID-19 pandemic is crucial for optimizing resource allocation and ensuring public safety. Current models lack the incorporation of major disruptions, leading to a critical gap in urban traffic forecasting. This study addresses this gap by developing an adaptive forecasting model, namely hyperparam-eter finetuning-convolutional neural network-multivariate-long short-term memory. Using hourly pedestrian counts (2020–2022) and COVID-19 case data, historical trends are integrated with pandemic factors to capture long-term patterns and sudden disruptions. Based on three evaluation metrics (NRMSE, MAPE, and R2), the results demonstrate the accuracy of the model in predicting pedestrian traffic during disruptive events. This research advances context-aware pedestrian traffic predictive modelling, enabling informed decision-making in urban environments.