Active regions on the surface of the Sun are potential sources of solar flares. These include active regions that are not directly facing Earth, such as those on the Sun's far-side. However, unlike Earth-facing active regions, far-side active regions including the east limb are difficult to observe. Therefore, east limb active region observation and analysis capability are necessary to predict the occurrences of east limb flares. The purpose of this study is to develop an automatic detection system of active regions occurring in the solar east limb using EUV intensity data from the EUVI instrument onboard the STEREO-A and STEREO-B spacecraft. Random forest and support vector machine algorithms were used in the system development. Active regions will be detected as bright regions in these EUV data. This detection system supports routine space weather forecasting activities at the Research Center for Space, part of the Space Weather Information and Forecast Services (SWIFtS). The system combines STEREO/EUVI with SDO/AIA data into a single composite map in the heliographic coordinate system, and then automatically detects the presence of east limb bright regions based on their intensities in near real-time. It also provides the estimated time of arrival (ETA) of the east limb bright regions on the east limb of the solar disk. The system also calculates the bright regions' mean intensity, total intensity, area and angular width. A machine-learning model has also been developed based on eight years of data to classify bright regions as different types of photospheric entities according to the calculated parameters. A validation test using an operational dataset produced accuracy scores of 79% and 64% for the random forest and support vector machine algorithms, respectively. [ABSTRACT FROM AUTHOR]