Acute Upper Respiratory Tract Infections (URTIs) represent a significant public health burden, particularly in conflict-affected regions such as Kharkiv Oblast, Ukraine. The ongoing conflict has disrupted healthcare services and increased the population’s vulnerability to infectious diseases. This study aimed to forecast the incidence of AURTIs using an Autoregressive Integrated Moving Average (ARIMA) model to support public health planning and intervention strategies. Monthly incidence data from January 2013 to April 2024 were collected from the Kharkiv Oblast Centre for Disease Control and Prevention. The ARIMA model, optimized for best fit, was used to predict future trends in AURTI cases. The model achieved a reasonable Mean Squared Error (MSE) of 0.2828, capturing general seasonal fluctuations, although it showed limited accuracy in forecasting sudden changes in incidence. The results indicate the model’s utility for anticipating overall seasonal patterns, helping public health authorities allocate resources effectively. However, the study also highlights the limitations of the ARIMA model in capturing sharp, unpredictable peaks, suggesting the need for more advanced approaches in future research to enhance forecasting accuracy in unstable environments.