The escalating impacts of global climate change and extreme weather have intensified flood risks worldwide, including in arid and semi-arid regions traditionally considered low-risk. This study examines the spatiotemporal dynamics of flood events across Kazakhstan from 2000 to 2024 by integrating remote sensing (RS) with machine learning (ML). Using Google Earth Engine (GEE), we address data gaps and cloud interference through spatiotemporal fusion (STARFM), denoising, smoothing, and sample transferring techniques. In addition, this study incorporates the TimeDisaggregated Water Frequency (TWF) method, which enables the identification of water bodies with temporal variability, eliminates permanent water bodies, and distinguishes flood from non-flood conditions in seasonal water bodies, thereby enhancing the accuracy of flood reconstruction and enabling precise delineation of flood inundation areas. Landsat and MODIS imagery are combined to produce high-resolution flood distribution maps, while spectral similarity indicators guide the transfer of samples from the Global Flood Database. A range of spectral, texture, environmental, and socioeconomic features is extracted, with flood classification performed using random forest (RF) and attribution analysis conducted via XGBoost and SHAP. Results highlight a high flood risk in northern, southwestern, and western Kazakhstan, primarily driven by changes in precipitation (PRE), temperature (TEM), soil moisture (SM), and land use. Floods occur most frequently in spring - especially in March and April - due to snowmelt and extreme precipitation. The ML models achieve over 80 % classification accuracy, demonstrating their reliability. This work improves flood monitoring and provides essential insights for climate adaptation and targeted flood risk management in Kazakhstan.