Türkiye is located in a seismically active region where earthquakes occur frequently. Unfortunately, recent seismic events have resulted in substantial loss of human life and damage in infrastructures. The February 2023 Kahramanmaraş earthquakes resulted with more than fifty thousand fatalities and more than hundred billions of dollars economic damage. Therefore, it is crucial to strengthen the existing structures and increase their seismic resilience in order to reduce such devastating results. Thus, a comprehensive study for evaluating the seismic performance of buildings, determination of risk levels, and prioritizing them for the further processes, is required. However, conducting detailed analyses for each building is extremely time-consuming and resource-intensive. In this context, it becomes more appropriate to first use the rapid assessment methods for an existing stock then support via detailed evaluations for those identified as risky. In this study, a rapid and practical framework named QuakeAssess has been introduced to prioritize the potential seismic risk of low-to mid-rise reinforced concrete buildings. Framework fundamentally comprises a two-stage assessment process. In the first stage, commonly-used rapid visual screening methods are implemented to get a preliminary decision. The second stage is based on a more detailed evaluation process that requires additional building information. Moreover, the results of the tool were crosschecked with a machine learning-based approach. Specifically, an Extreme Gradient Boosting algorithm is trained on a dataset composed of buildings with known seismic performances. Here, a dual-stage framework with a user-friendly interface is expected to ensure both speed and reliability in preliminary seismic risk evaluation for existing large building stocks.