State-of-the-art storage devices that have parallel capability have significantly reduced the performance gap between processor and storage I/O. However, the internal parallelism makes it difficult to measure utilization that can be used as a basis of load balancing, which is a critical feature of performance improvement of parallel systems. When utilization of storage reaches to one hundred percent, the I/O saturation occurs, and then some of I/O loads need to be redirected to the other storage to improve the whole storage performance. There is, to my best knowledge, no studies in I/O saturation prediction of the flash SSDs regarding the internal parallelism that the previous HDD based measure cannot reflect. In this paper, we propose I/O saturation prediction method based on ANN (Artificial Neural Network) using kernel I/O statistics and various performance measures. We extracted I/O statistics and performance measures by conducting I/O workload simulation especially on NVMe (Non-Volatile Memory Express) flash SSD so that we can get as many observations from various I/O characteristics as flash SSD possibly can show. We constructed ANN model and compared with SVM (Support Vector Machine) model. The evaluation shows that ANN performs well compared to the existing utilization measure which assumes that HDD can only perform single instruction at a time.