This research supports solving the problem of how to know the next movement of the nanobubble, for this purpose, two methods will be used to represent the behavior of air nanobubbles in liquids, such as the correlation of data expressed in an equation and the backpropagation in learning your route. To obtain the positions of the movements of the air nanobubbles at a scale of 10-9 m in diameter, algorithms based on computer vision, using high-power cameras. The correlation of data was identified to generate the equation and the neural network to learn its movements. In conclusion, the behavior of nanobubbles in water was identified, generating a specific movement pattern of y = 9E-06x3 - 0.0034x2 + 1.6831x + 299.25; with a correlation of: R2 = 0.9976 managing to obtain a 98.94% certainty, and it was possible to learn these movements by generating the appropriate synaptic weights with a 99.6% certainty in prediction their route or next path.