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Global emergency departments and urgent care facilities regularly experience prolonged waiting periods that surpass patient expectations, primarily driven by unexpected spikes in patient volumes. The resulting delays caused by insufficient staffing during these high-demand intervals have been associated with detrimental clinical consequences. Prior investigations into predicting patient volumes have predominantly relied on traditional statistical approaches. Furthermore, these efforts have mainly focused on short-range predictions, which offer minimal utility for the long-term scheduling of medical staff. This paper contributes to a growing domain of research aimed at investigating the capacity of machine learning frameworks to produce precise patient volume projections. Utilizing a decade's worth of data from two major urgent care centers, this study constructs long-range patient flow predictions extending up to one quarter in advance by evaluating a selection of modern, high-performing algorithms. A unique aspect of this research is the integration of eXplainable Artificial Intelligence (XAI) frameworks, specifically LIME and Shapley, to conduct a comprehensive assessment of internal model dynamics that are typically obscured. These diagnostic instruments allowed us to examine how effectively the predictive frameworks adjusted to volatile demand shifts induced by COVID-19 pandemic lockdowns, while also determining the most significant predictor variables to gain deep operational insights. The empirical findings indicate that an innovative ensemble combining gradient boosting techniques with advanced univariate frameworks like Prophet yielded the most precise and dependable performance overall. On average, this methodology produced a 16%–30% improvement over the current internal forecasting techniques utilized for predicting daily patient volumes 90 days into the future.