Forecasting complex, multivariate time series using machine learning presents significant challenges, particularly when dealing with high-dimensional biological data. One such application is the prediction of microbial community dynamics in wastewater treatment plants (WWTPs), which is directly relevant to public and environmental health. Previously, we addressed this problem by training different model architectures and comparing their performance. In this study, an attempt was made to enhance model performance by adding operational and environmental metadata, such as temperature, inflow rates, and nutrient levels. The parameters were incorporated into the time series, and their influence was systematically evaluated. The findings indicate that while metadata enhances predictive accuracy, the significance of individual features varies across WWTPs. Training a model on one dataset and retraining that model on site-specific data has been demonstrated to enhance performance, underscoring the necessity for adaptive, localized modeling strategies. The findings demonstrate the value of machine learning approaches for extracting predictive insights from complex, high-dimensional biological time series data in wastewater systems. The implementation of such a system as an additional wastewater monitoring technique has the potential to facilitate more timely public health decision-making in the future.