University Examination Timetabling Problem is the most important combinational problem to develop a conflict-free timetable to execute all of the exams in and with the limited timeslots and other resources for universities, colleges or schools. It is also an important Nondeterministic Polynomial Time (NP)-hard problem that has no deterministic solution, so it is an important study to develop an optimised solution to satisfy all of the hard and soft constraints required to create a timetable that would be flexible for all stakeholders such as students, teachers, invigilators, as well as management. Several heuristics, meta-heuristics and mixed integer programming approaches have been performed in the past to develop a solution for it, but use of hybrid techniques and hyper-heuristics through implementing Artificial Intelligence (AI) improve the efficiency and performance in solving the problem using the single or multiple objective function. In this systematic review, we are going to learn the effectiveness of these heuristic and meta-heuristics techniques in solving the examination problem as well as how these can be merged to develop a more effective and efficient hybrid solution. The impact of AI is also reviewed in developing the solution for the examination timetabling problem. While analysing papers included in this SLR, various research gaps are identified including the level of hybridisation between population-based and local-search techniques, designing an effective algorithm for an initial solution, providing a balanced solution for multiple stakeholders, and developing a phase-wise and partial solution to improve the effectiveness of a final algorithm for the university examination timetabling problem. It provides insight into the various challenges and opportunities as well as future directions to develop or to perform research in providing a solution for it. It is identified that the use of machine-learning, reinforcement learning, and deep-learning with more advanced AI techniques can optimise and to improve the performance of a solution for the university examination timetabling problem.