Red palm weevil (RPW) is a harmful pest that has wiped out many palm plantations worldwide. Early detection of RPW is difficult, especially on large plantations. Here, we report on combining fiber–optic distributed acoustic sensing (DAS) and machine learning to detect weevil larvae less than three weeks old, in a controlled environment. In particular, we use the temporal and spectral data provided by a fiber–optic DAS system to train a convolutional neural network (CNN), which distinguishes “healthy” and “infested” signals with a classification accuracy higher than 97%. Additionally, a rigorous machine learning classification approach is introduced to improve the false alarm performance metric by >20%.