Scientific workflows deserve the emerging attention in sophisticated large-scale scientific problem-solving environments. Though a single task failure occurs in workflow based applications, due to its task dependency nature the reliability of the overall system will be affected drastically. Hence rather than reactive fault tolerant approaches, proactive measures are vital in scientific workflows. This work puts forth an attempt to concentrate on the exploration issue of structuring an Exotic Intelligent Water Drops - Support Vector Regression-based approach for task failure prognostication which facilitates proactive fault tolerance in scientific workflow applications. The failure prediction models in this study have been implemented through SVR-based machine learning approaches and its precision accuracy is optimized by IWDA and various performance metrics were evaluated. The experimental results prove that the proposed approach performs better compared with the other existing techniques.