  Recent advances in natural language processing (NLP) may enable artificial
intelligence (AI) models to generate writing that is identical to human written
form in the future. This might have profound ethical, legal, and social
repercussions. This study aims to address this problem by offering an accurate
AI detector model that can differentiate between electronically produced text
and human-written text. Our approach includes machine learning methods such as
XGB Classifier, SVM, BERT architecture deep learning models. Furthermore, our
results show that the BERT performs better than previous models in identifying
information generated by AI from information provided by humans. Provide a
comprehensive analysis of the current state of AI-generated text identification
in our assessment of pertinent studies. Our testing yielded positive findings,
showing that our strategy is successful, with the BERT emerging as the most
probable answer. We analyze the research's societal implications, highlighting
the possible advantages for various industries while addressing sustainability
issues pertaining to morality and the environment. The XGB classifier and SVM
give 0.84 and 0.81 accuracy in this article, respectively. The greatest
accuracy in this research is provided by the BERT model, which provides 0.93%
accuracy.
