Using acoustic waves to estimate fluid concentration is a promising technology due to its practicality and non-intrusive aspect, especially for medical applications. The existing approaches are exclusively based on the correlation between the reflection coefficient and the concentration. However, these techniques are limited by the high sensitivity of the reflection coefficient to environment conditions changes, even slight ones. This introduces inaccuracies that cannot be tolerated in medical applications. This paper proposed a deep learning model, Fluid Concentration Estimation Convolutional Neural Network (FCE-CNN), to estimate fluid concentration. Instead of using only the reflection coefficient, we train our model to detect concentration-related patterns based on the whole received acoustic signal. FCE-CNN shows promising results that overcome the state-of-the-art limitations. Specifically, our model that is able to estimate fluid concentration with 98.5% accuracy using ultra high frequency acoustic waves.