Social networking platforms gained widespread popularity and are used for various activities like: promoting products, sharing news, achievements and many more. On the other hand, it is also used for spreading rumors, bullying people, and abusing certain groups of people with hateful words. The hate and offensive posts must be detected and removed as early as possible from the social platforms because such posts are spread very quickly and tend to have a lot of negative impacts on human beings. In the last few years, offensive content and hate speech detection has become popular topic of research. Detecting hate speech on social platforms has many challenges, one of them being the use of code-mixed language. Majority of the social media users usually post their messages in code-mixed languages such as Hindi–English, Tamil–English, Malayalam–English, Telugu–English and others. In this exhaustive study, we explore and compare the use of various machine learning and deep learning approaches. An ensemble model by combining the outcomes of transformer and deep learning-based models is suggested to detect hate speech and offensive language on social networking platforms. The experimental outcomes of the proposed weighted ensemble framework outperformed state-of-the-art models by achieving 0.802 and 0.933 weighted F1-score for Malayalam and Tamil code-mixed datasets. •Proposed a weighted ensemble framework for hate and offensive code-mixed posts identification on social platforms.•Two code-mixed datasets, namely Tamil and Malayalam, are used in this research.•The proposed model utilized the outcomes of deep learning and transformer-based models.•Transformer based models like m-BERT, distilBERT, xlm-RoBERTa performed better than the ML and DL based models.