Field research stations in Nigeria are crucial for environmental monitoring and management, yet their effectiveness varies widely. A systematic review was conducted to identify, assess, and analyse existing studies that employed various methodologies for monitoring environmental variables at Nigerian research stations. Studies were selected based on their relevance to clinical outcomes measurement using time-series forecasting techniques such as ARIMA (AutoRegressive Integrated Moving Average). The analysis revealed a significant proportion (30%) of studies used ARIMA models with robust standard errors indicating the reliability of these methods for predicting environmental changes impacting health outcomes. ARIMA modelling offers a promising approach for forecasting clinical outcomes in Nigerian research stations, providing a structured framework to enhance data accuracy and predictive power. Further empirical studies should explore ARIMA applications with larger datasets to validate model predictions across different geographical locations and environmental variables. Field Research Stations, Nigeria, Time-Series Forecasting, Clinical Outcomes Measurement, ARIMA Model The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.