Food Security is a crucial global concern and a key pillar of the Sustainable Development Goals (SDG 2 - Zero Hunger). To monitor food insecurity situations, several early warning systems are active today, driven by agencies such as FAO and WFP. These systems use a limited set of data types, e.g., agroclimatic data and indicators extracted from household surveys. Household surveys are expensive and time-consuming, and by consequence not frequent, which ultimately limits the ability to provide timely responses in vulnerable regions. In this work, we present a deep learning (DL) framework able to classify regional food security levels into three main categories i.e., poor, borderline and acceptable. The idea is to obtain such predictions by using openly accessible heterogeneous data sources, including satellite imagery, climate variables, and socioeconomic indicators. The proposed architecture employs multi-branch neural network models—Convolutional Neural Networks (CNNs) for spatial rasters (population density, land use/land cover maps), Recurrent Neural Networks for time series (e.g., rainfall, temperature, commodity prices), and Random Forests for feature fusion that integrates representations extracted from the two DL branches with a set of conjunctural variables. We validate our approach on two African countries, Burkina Faso and Rwanda, achieving classification accuracies exceeding 85% using different experimental settings. This demonstrates the potential of our model to support real-time food security assessments at subnational levels, by representing an effective and affordable complement to existing early warning systems.