Numerical modeling of non-inertial particles dynamics is usually addressed by solving a population balance equation (PBE). In addition to space and time, a discretization is required also in the particle-size space, covering a large range of variation controlled by strongly nonlinear phenomena. A novel approach is presented in which a hybrid stochastic/fixed-sectional method solving the PBE is used to train a combination of an artificial neural network (ANN) with a convolutional neural network (CNN) and recurrent long short-term memory artificial neural layers. The hybrid stochastic/fixed-sectional method decomposes the problem into the total number density and the probability density function of sizes, allowing for an accurate treatment of surface growth/loss. After solving for the transport of species and temperature, the input of the ANN is composed of the thermochemical parameters controlling the particle physics and of the increment in time. The input of the CNN is the shape of the particle size distribution (PSD) discretized in sections of size. From these inputs, in a flow simulation, the ANN–CNN returns the PSD shape for the subsequent time step or a source term for the Eulerian transport of the particle size density. The method is evaluated in a canonical laminar premixed sooting flame of the literature, and for a given level of accuracy (i.e., a given discretization of the size space), a significant computing cost reduction is achieved (six times faster compared to a sectional method with ten sections and 30 times faster for 100 sections).