Abstract: The detection of stellar flares is crucial to understanding dynamic processes at the stellar surface and their potential impact on surrounding exoplanetary systems. Extensive time series data acquired by the Transiting Exoplanet Survey Satellite (TESS) offer valuable opportunities for large-scale flare studies. A variety of methods is currently employed for flare detection, with machine learning (ML) approaches demonstrating strong potential for automated classification tasks, particularly for the analysis of astronomical time series. This review provides an overview of the methods used to detect stellar flares in TESS data and evaluates their performance and effectiveness. It includes our assessment of both traditional detection techniques and more recent methods, such as ML algorithms, highlighting their strengths and limitations. By addressing current challenges and identifying promising approaches, this manuscript aims to support further studies and promote the development of stellar flare research.