The integrated approach of utilizing social network analysis and machine learning techniques to mine social network data has become a challenging and increasingly popular domain in machine learning research, and the pace of interdisciplinary research is accelerating. It entails gathering, processing, and analysing large volumes of data from social media platforms like Twitter, Facebook, Instagram, and LinkedIn. Social network mining with machine learning aims to extract useful insights from the data so that decision-making and the user experience can be enhanced. This could involve issues such as identifying influencers, predicting user behaviour, detecting spam and bots, and understanding user sentiment in discussions. Machine learning can be used in a variety of applications for social network mining including in areas such as marketing, analysis of public opinions to social media management. However, there are also some ethical concerns around using the technology, most notably in relation to bias and privacy issues. This paper explores how the machine learning-based social network mining offers much promise, potentially bringing substantial insight into user behaviour and social trends.