Spam email detection is a crucial aspect of maintaining the integrity and usability of email systems. Traditional methods often struggle to adapt to the evolving tactics used by spammers, necessitating advanced techniques to enhance detection capabilities. The present article introduces a hybrid method that effectively detects spam emails by combining the Monarch Butterfly Optimization and Spider Monkey Optimization algorithms. By leveraging the complementary strengths of MBO and SMO, we aim to optimize the selection of relevant features from both text-based and image-based spam datasets, namely the Spam Base and Image Spam Hunter datasets. The MBO algorithm, inspired by the migration behavior of monarch butterflies, excels in exploration, while the SMO algorithm, modeled on the foraging behavior of spider monkeys, provides robust exploitation capabilities. The proposed hybrid approach is evaluated using standard performance metrics, demonstrating important advances in classification accuracy, and F1-score over traditional feature selection methods. This research highlights the potential of metaheuristic algorithm combinations in addressing the complexities of spam email detection.