In　this　investigation,　we　propose　a　solution　for　the　author＇s　gender　identification　task　called　AGI-P.　This　task　has　several　real-world　applications　across　different　fields,　such　as　marketing　and　advertising,　forensic　linguistics,　sociology,　recommendation　systems,　language　processing,　historical　analysis,　education,　and　language　learning.　We　created　a　new　dataset　to　evaluate　our　proposed　method.　The　dataset　is　balanced　in　terms　of　gender　using　a　random　sampling　method　and　consists　of　1944　samples　in　total.　We　use　accuracy　as　an　evaluation　measure　and　compare　the　performance　of　the　proposed　solution　(AGI-P)　against　state-of-the-art　machine　learning　classifiers　and　fine-tuned　pre-trained　multilingual　language　models　such　as　DistilBERT,　mBERT,　XLM-RoBERTa,　and　Multilingual　DEBERTa.　In　this　regard,　we　also　propose　a　customized　fine-tuning　strategy　that　improves　the　accuracy　of　the　pre-trained　language　models　for　the　author　gender　identification　task.　Our　extensive　experimental　studies　reveal　that　our　solution　(AGI-P)　outperforms　the　well-known　machine　learning　classifiers　and　fine-tuned　pre-trained　multilingual　language　models　with　an　accuracy　level　of　92.03%.　Moreover,　the　pre-trained　multilingual　language　models,　fine-tuned　with　the　proposed　customized　strategy,　outperform　the　fine-tuned　pre-trained　language　models　using　an　out-of-the-box　fine-tuning　strategy.　The　codebase　and　corpus　can　be　accessed　on　our　GitHub　page　at:　https://github.com/mumairhassan/AGI-P