This research explores issues in transforming spoken or written colloquial dialects into standard Arabic using artificial intelligence and machine learning techniques. It is part of the project, “Developing Applications to Correct Jordanian Spoken Arabic to Proper Language Using Machine Learning Techniques.” The paper addresses two interconnected dimensions. The computational linguistic dimension provides a technological framework for processing language using deep machine learning techniques. The linguistic dimension examines the interactions between colloquial Jordanian Arabic and Modern Standard Arabic (MSA). It analyzes changes in phonetics, morphology, orthography, syntax, rhetoric, and semantics. The study focuses on processing real-world texts derived from society’s linguistic reality. It uses the Jordanian dialect as a comprehensive model for bridging colloquial and MSA. The goal is to develop applications that preserve and promote the use of eloquent and correct linguistic content. This applies to daily communication and written dialogues on technological platforms. The success of these applications in addressing the Jordanian dialect could inspire similar projects across other Arab countries. This would enhance cultural communication and linguistic preservation on a broader scale.​