Evolutionary and neural computing are two prominent paradigms in computational intelligence that have been extensively researched and applied in various domains. Evolutionary algorithms, inspired by the principles of natural selection and genetics, employ mechanisms such as selection, mutation, and crossover to evolve solutions to optimization and search problems. On the other hand, neural computing draws inspiration from the structure and function of biological neural networks to develop artificial neural networks (ANNs) capable of learning from data and performing tasks such as classification, regression, and pattern recognition. This chapter provides a comprehensive review of the intersection between evolutionary and neural computing, highlighting their complementary strengths and synergies. It discusses the integration of evolutionary algorithms with neural network training and optimization processes, including techniques such as neuro evolution and evolutionary optimization of neural network architectures. Moreover, it explores how evolutionary principles can be leveraged to enhance the design and training of neural networks, addressing challenges such as the curse of dimensionality, overfitting, and exploration-exploitation trade-offs.