This talk explores the intersection of bio-inspired computation, machine learning, and deep learning, focusing on how nature-inspired optimization algorithms can support neural-network design, hyperparameter tuning, and training. It discusses the strengths of bio-inspired methods for complex, high-dimensional, multimodal, and non-differentiable optimization problems, while also addressing key challenges such as computational cost, scalability, and fair algorithm comparison. The talk highlights important research practices, including realistic benchmarking, statistical significance testing, comparison with established gradient-based optimizers, and evaluation of computational complexity. It concludes by outlining promising future directions such as hybrid optimization strategies, adaptive parameter control, stronger theoretical foundations, standardized evaluation methodologies, energy-efficient computing, and emerging computing paradigms.Type your paragraph here