The escalating computational demands of deep learning models pose significant challenges for deployment on resource-constrained devices. This paper introduces a novel optimization framework that synergizes tropical algebra with neural network quantization to achieve substantial computational efficiency gains in Tropical Convolutional Neural Networks (TCNNs). The structural optimization of tropical algebra’s unique ability to replace multiplication operations with addition in TCNNs, is further enhanced by applying advanced parameter quantization techniques, including Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), Learned Step-Size Quantization (LSQ), and DoReFa quantization. Through extensive experiments on CIFAR-10 and SVHN datasets, we demonstrate that our Quantized Tropical Convolutional Neural Networks (QTCNNs), based on ResNet18/34 architectures, achieve competitive or superior performance compared to both quantized standard CNNs and full-precision networks, while substantially reducing storage requirements and computational costs. Our systematic analysis reveals that combining tropical algebra with quantization creates synergistic optimization effects that neither approach can achieve independently, offering a promising direction for efficient deep learning deployment in resource-constrained environments. Code is available at https://github.com/luoye-group/QTCNNs.