Clinical outcomes in field research stations are influenced by various environmental factors such as soil health and climate variability. The study employed a multivariate time-series analysis model to forecast clinical outcomes based on environmental indicators collected over several years. A linear regression model was found to be the most accurate in predicting clinical outcomes, with an R² value of 0.85 and robust standard errors indicating the reliability of the model’s predictions. The time-series forecasting models demonstrated significant potential for improving the prediction accuracy of clinical outcomes in Nigerian field research stations. Further research should focus on validating these findings across different geographical regions and incorporating additional environmental factors to enhance predictive precision. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.