Halide perovskites are positioned at the forefront of photonics, optoelectronics, and photovoltaics, owing to their excellent optical properties, with emission wavelengths ranging from blue to near-infrared, and their ease in manufacturing. However, their vast composition space and the corresponding emission energies are still not fully mapped, and guided high-throughput screening that allows for targeted material synthesis would be desirable. To this end, we use experimental data from the literature to build a machine learning model, predicting the band gap of 10,920 possible compositions. Focusing on one of the most promising candidates, Cs