Active inference is a theory of the human brain characterising behaviour that minimises surprise. The free energy principle accounts for the adaptive behaviours of organisms through action, perception, and learning aimed at optimising reward or surprise. This study systematically reviews relevant literature to address their methodologies, relevance to mimicking social human behaviour, challenges, and limitations to guide future research by succinctly reporting previous findings and research gaps. Active inference models are extended with deep active inference, free-energy models, multimodal deep belief networks, predictive coding, and probabilistic programming. These models employ goal-directed, epistemic, reward-seeking, and decision-making behaviours and simulate cumulative culture. However, some of these models do not translate well to complex real-life applications due to their simplicity, computational demands, or the assumptions upon which they are based. Challenges with real-life applications include difficulty scaling to high-dimensional data and model simplicity. Furthermore, some experiments did not have enough data to validate or train their models.