Neural networks have significantly advanced image recognition, enabling applications in autonomous vehicles, surveillance, healthcare, and augmented reality. However, achieving real-time performance remains a challenge due to computational complexity, latency, and energy constraints. This paper explores optimization techniques that enhance neural network efficiency while maintaining high accuracy in real-time image recognition tasks. Key strategies for optimization include model compression techniques such as pruning, quantization, and knowledge distillation, which reduce computational overhead while preserving accuracy. Algorithmic improvements, including early exit strategies, efficient activation functions, and optimized convolutional operations, further accelerate inference speed. Additionally, hardware acceleration using GPUs, TPUs, and FPGAs provides significant performance gains, enabling real-time processing even for complex neural architectures.