Artificial Intelligence (AI) has emerged as a powerful and effective tool with several applications in health science. An inherent drawback of drug therapies is the potential for side effects, which are adverse reactions that negatively impact human health. In recent years, AI has been applied in pharmacology and pharmacovigilance, e.g., for studying and analysing drug side effects. Likewise, network science has become widely used as an effective and efficient tool for modelling interactions between biological objects. In this paper, we presented a framework for predicting candidate drug side effects by using Machine Learning (ML) techniques applied to biological multilayer networks. Experimentation supports the application of the ML-based models implemented in the proposed framework for predicting novel (candidate) drug side effects from biological multilayer networks.