Abstract : Executing Join-Aggregate queries on Big Data can incur enormous computational costs. Hence, the Approximate Aggregate Query Processing Techniques (AQPTs) are an attractive choice to execute such join-aggregate queries, because they incur limited computational costs. The AQPTs utilize random sampling to approximately execute a given join aggregate query. However, the effectiveness of random sampling is highly correlated with the number of qualifying tuples of the given query. If the given query has limited number of qualifying tuples, the approximate answer obtained by AQPT can suffer from poor approximation accuracy. Hence, it is extremely necessary to analyze the feasibility of a given join-aggregate query to be executed by AQPT. In the literature, such feasibility framework has not received any attention. Hence, in this paper, a feasibility framework for analyzing the feasibility of a given join-aggregate query to be executed by AQPT, is presented. This proposed feasibility framework is designed through the aid of a probabilistic model. An empirical analysis study of the proposed feasibility framework is presented. In this study, the proposed feasibility framework demonstrates appreciable performance in-terms of prediction accuracy and execution latency. The proposed feasibility framework presents a significant advancement in the field of Approximate Aggregate Query Processing AI Techniques. Its effective utilization of a probabilistic model for feasibility analysis provides a reliable and efficient solution for executing join-aggregate queries, paving the way for more optimized and cost-effective Big Data processing.