Mobile Ad-Hoc Network (MANET) is a wireless network, which are connected in a self-healing and self-configured manner without any fixed infrastructure. One of the main challenges in MANETs is the development of an efficient Clustering and Routing Algorithm (CRA) that can select Cluster Heads (CH) and paths based on performance metrics related to node or path qualities. Various CRAs have been developed using metaheuristic algorithms like Grey Wolf Optimization (GWO), Ant Colony Optimization (ACO), etc. However, most of these algorithms focus on a single objective to improve clustering and routing performance based on specific metrics like delay or energy usage. Hence, this manuscript introduces a novel Multi-Objective Hippopotamus Optimization Algorithm-based CRA (MOHOA-CRA) for MANETs. This algorithm aims to address the challenges of efficient CH selection and optimal path determination in dynamic MANET environments. It considers multiple factors such as node density, energy consumption, mobility and hop count to optimize network performance. It executes two main phases: optimal CH selection and path selection, both leveraging the capabilities of the HOA. Extensive simulations comparing MOHOA-CRA with existing algorithms demonstrate its superior performance across various metrics including energy consumption, Normalized Routing Load (NRL), throughput, End-to-End Delay (E2D), and packet loss. The results show significant improvements, especially in networks with high node counts. For example, in a network with 1000 nodes, MOHOA-CRA achieves a mean energy consumption of 15.4%, NRL of 60, throughput of 131 Kbps, mean E2D of 200 ms, and packet loss of 8.5%, outperforming existing algorithms.