The Red Palm Weevil (RPW) is one of the major and highly destructive palm tree pests around the world that caused a tremendous economic loss and environmental damage. This thesis introduces a unique method for early detection using the latest research findings in the area of deep learning that could introduce a revolutionary solution to the characteristic. The model takes a dual approach: one comprehensive pre-processing stage for eliminating acoustic data from attacked RPW sites and the second phase adjusts the model using data augmentation technique to enhance its robustness and generalization abilities. The above model made use of the transfer learning concept to deploy and consume the novel advanced neural network architecture, EfficientNetV2S and DenseNet169, in the training and evaluation stages using a dataset of audio signals from the RPW-attacked and healthy palm trees During the experiment, the classification performance of these models was tested based on the overall detection outcome. Our results demonstrated that EfficientNetV2S outperforms DenseNet169. Most importantly, this thesis is highly beneficial for the Аgricultural industry domain since it provides an advanced technology-based solution to the prevailing problem. The results shown in the current study open up a new line of research focused on the use of methodology when dealing with pest detection problems. To sum up, this opens up new possibilities not only for future Red Palm Weevil management activities but also for general app usages. It solidifies a strong potential as a suitable and optimal model created for the two mentioned architecture which could be efficient and applicable for the RPW detection in this project with 95.57% for EfficientNetV2S, 93.91% for DenseNet169, possibly inspiring the appearance of even more models and projects to work with Deep Learning techniques, demonstrating the potential of such abilities in detecting different patterns on acoustics levels indicated in RPW.