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Skilled nursing facilities (SNFs) in the U.S. serve over 1.5 million residents and experience continuous census volatility driven by admissions, discharges, and mortality, impacting staffing, bed availability, and care quality. Existing forecasting methods rarely capture these dynamics together, leading to reactive and inefficient operational decisions. A need exists for accurate, multi-horizon, and data-integrated forecasting systems. Traditional models like ARIMA and LSTM are limited in SNF census forecasting because they produce single-point estimates, fail to model uncertainty, and cannot effectively integrate heterogeneous data such as facility characteristics, temporal utilization patterns, and external factors like COVID-19 prevalence. They also lack interpretability, reducing their usefulness for decision-making. This study introduces an attention-based Temporal Fusion Transformer (TFT) for multi-horizon SNF census forecasting (1, 7, 14, and 30 days). It integrates admissions, discharges, and COVID-19 prevalence through dedicated encoders and applies variable selection networks, LSTM layers, and multi-head attention to capture temporal dependencies and feature importance. The model outputs quantile forecasts (10th, 50th, 90th percentiles) to quantify uncertainty. The TFT enhances interpretability by identifying which past events and features most influence predictions at each horizon, enabling administrators to understand how admissions trends, discharge patterns, and COVID-19 surges affect census dynamics. The proposed framework enables proactive SNF capacity planning by combining multi-source data with interpretable, uncertainty-aware forecasting, supporting a shift from reactive staffing to anticipatory resource allocation and improved operational efficiency.