Feature tracking is an efficient method for estimating glacier velocity by identifying the surface displacement between image pairs through maximum normalized cross-correlation (NCC). However, this method may misidentify displacement when noise is present in one or both images or when natural causes change glacier morphology. To improve accuracy, we developed the Google Earth Engine Glacier Velocity (GEEG-Vel) estimation method, which utilizes image enhancement and multi-image pair NCC maximization. GEEG-Vel results are further filtered using PyFilter, a Python routine that improves the glacier velocity estimation by utilizing velocity pairs obtained from GEEG-Vel. The combination of GEEG-Vel and PyFilter provides an efficient and accurate approach for glacier velocity estimation across various types of datasets, including optical and SAR data. We compared the results with the ITS_LIVE glacier velocity for the same period (2013-2018), and the mean velocity difference for each year was less than 10 m/year. Our study demonstrates that the combination of GEEG-Vel and PyFilter provides a reliable and accurate approach for glacier velocity estimation, which can be useful for monitoring the dynamics of glaciers and their response to climate change.