Currently, social media platforms include numerous, namely Facebook, Twitter, Instagram, blogs, reviews and news websites that permit people to share their opinions and reviews. Twitter is one of the vital sources of information for obtaining people’s attitudes, feelings, opinions and feedback. Within this context, Twitter sentiment analysis models are proposed in order to select whether textual tweets prompt a positive or negative opinion. The use of machine learning (ML) in tweets classification updates procedure of data retrieval on social media platforms as well as simplifies insights into public trends, opinions, and emerging subjects. This manuscript offers a new Barnacles Mating Optimizer with Machine-Learning-Assisted Tweets Classification (BMOML-TC) technique for Sustainable Urban Living. The BMOML-TC technique intended to boost sustainable urban living via effective investigation of social networking data. BMOML-TC technique leverages preprocessing and Bidirectional Encoder Representations from Transformers (BERT), to extract complex features relevant to sustainable urban living. Moreover, beta Variational Autoencoder (VAE) utilized for classification of the tweets. Finally, BMO algorithm applied for automated and accurate parameter tuning. The performance evaluation of BMOML-TC model authorized on benchmark dataset. An experimental outcomes stated significance of BMOML-TC model on recognition and identification of tweets on social media platforms.