Protein-protein interactions within a cell are essential for various fundamental biological processes. Computational techniques have arisen in bioinformatics due to the challenging and resource-intensive nature of experimental protein pair interaction studies. This research seeks to create a cutting-edge machine learning method for predicting protein pair interactions using carefully chosen input features and leveraging evolutionary data. PPILS leverages evolutionary knowledge from the protein language model. It develops an encoder-decoder architecture with light attention. The trained model obtains protein embeddings from a language model and employs a light attention-based encoder, where a single convolution operation generates attention. A subsequent convolution is applied to input features, creating a representative construct for the protein interaction prediction. These encoded representations are then channeled into the decoder to predict protein interactions. Our findings indicated that PPILS outperformed existing methods in PPI prediction. The proposed method could be essential in protein-protein interaction prediction, further accelerating the discovery of protein-based drugs.