Mobile Ad hoc Network is a self-organizing, infrastructure-free, distributed wireless networks made up of various mobile devices. Quality-of-Service routing is most difficult task in MANET due to inherent characteristics–for example frequent dynamic topology, node mobility, resource scarcity, absence of centralized control, etc as well. The QoS variables of any MANET routing algorithms determine its performance. QoS routing is process of routing packets from source (S) to destination (D) based on QoS resource constraints such as bandwidth, delay, packet loss rate, cost, security, link stability, and so on. Swarm intelligence, which mimics the collective behaviour of biological organisms to handle routing problems and improve QoS in the network, has been one of most popular studies for network routing in recent years. Particle Swarm Optimization algorithm (PSO), Genetic Algorithm (GA) and Ant Colony Optimization algorithm (ACO) have all been shown to be effective for developing routing algorithms by improving QoS metrics in ad hoc networks using Swarm Intelligence (SI). The primary objective of this comparative study paper is to improve QoS parameters by applying swarm intelligence to MANET routing algorithms. Swarm intelligence-based routing algorithms will be more promising for the specific nature of adhoc networks, outperforming in real scenarios/constraints/environmental conditions and will be tuned and simulated to obtain an efficient and effective MANET routing protocol. This paper investigates four potential pre-existing approaches proposed for MANET routing problems. These routing algorithms are evaluated using various performance metrics such as packet delivery ratio, routing overhead, link failure prevention, energy consumption, accuracy, and throughput, among others.