Abstract The Malaria remains a well-known public health challenge in Papua New Guinea (PNG), especially in low-altitude coastal cities, such as Lae, where the Papua New Guinea University of Technology (Unitech) is situated. Using time series regression analysis, this study models and predicts malaria infection rates at the University Health Services (UHS) ranging from 2018 to 2024. Monthly malaria case counts were sourced from health clinic registers and analysed with descriptive statistics and seasonal decomposition along with autoregressive integrated moving average (ARIMA) modelling with meteorological covariates (ARIMAX). Data of the original series were analysed using Augmented Dickey-Fuller (ADF) test; to detect non-stationarity and this was resolved via first-order differencing. The seasonal analysis indicated that malaria cases reached a consistent peak period in March-June, which is corresponding with the wet season in Morobe Province. SARIMA (1,1,1) (0,1,1)12 with rainfall as an exogenous variable yielded a mean absolute percentage error (MAPE) of 8.3%, indicating the best prediction accuracy. We find a statistically significant positive association between monthly rainfall and malaria incidence (p < 0.001). These findings complement evidence-based planning for malaria prevention and control interventions appropriate to the Unitech campus community. The work in this study adds to the expanding literature on epidemiological research using data in PNG and highlights the significance of campus health surveillance systems.