This study investigates how the fusion of natural language processing (NLP) and machine learning (ML) can improve marketing strategies and boost customer engagement. This integration offers precise consumer targeting and personalized engagement. We studied the impact of textual content in standard and dialectal Arabic on user engagement on Facebook by analyzing datasets of posts and associated comments. Utilizing (NLP), (ML), and sensitivity analysis, we explore how specific words and content features boost user interactions. We collected our data from Facebook, focusing on standard and dialectal Arabic text content and number of comments. By combining (ML) algorithms with (NLP), we identified the most important features influencing engagement through feature importance extraction. In addition, we were able to assess how changes in input features affected engagement outcomes by integrating (ML) techniques with sensitivity analysis. Our analysis reveals that certain linguistic features, and word choices significantly affect engagement levels. The study provides a framework for understanding the influence of standard and dialectal Arabic textual content on user engagement and offers insights for developing effective content strategies to enhance interaction on social media.