In recent times, many researchers have explored the prediction of infectious disease outbreaks using mathematical modeling, artificial intelligence, agent-based simulation models among other technologies. However, there is still a need to improve on the prediction accuracy of the epidemiological surveillance systems–since the emergence of outbreaks is due to the multi-level interactions of humans, pathogens, and environments. Cogent socio-ecological research efforts suggest that the phenomenon of infectious disease outbreaks is best tackled from the perspective of Complex Adaptive systems (CAS). In this study, we provided a scoping review of various approaches adopted in the literature for epidemiological surveillance systems– with the goal of creating a new pathway for a more robust surveillance model using a deep learning approach enhanced with an equilibrium state bifurcation technique for early, and a more accurate detection of infectious disease outbreaks in epidemiological surveillance systems.