This study employs the Eddy Covariance (EC) method and Long Short-Term Memory (LSTM) neural networks to analyze and predict sensible heat flux at a single station in Guandu Village, Wugang Town, Quanjiao County, Chuzhou City, Anhui Province, China. The selection of an appropriate detrending time window in the EC method is crucial for accurate interpretation and application. For predicting sensible heat flux, although the LSTM model performed well in short-term predictions, with a 3-h prediction correlation of 0.91 and a bias of −4.23 W/m2, its long-term prediction performance requires further optimization. Incorporating ERA5 reanalysis data, the study investigated the seasonal variations of sensible heat flux in Eastern and Central China from 2014 to 2023. Results indicate an increasing trend in sensible heat flux over the past decade, with the highest value observed in 2022. Seasonal analysis reveals that sensible heat flux is highest in spring and winter, particularly in low and mid-latitude regions (around 25 °N). The study also found that meteorological factors such as surface temperature, cloud cover, and precipitation positively influence sensible heat flux, while vegetation cover has a negative impact. This research underscores the critical role of sensible heat flux in energy exchange processes and offers valuable insights for enhancing meteorological models and climate predictions.