Robot manipulation requires policies that are both accurate and efficient, as robot control must respond to changing observations under tight latency constraints. Recent diffusion and flow policies are promising, but they often treat conditions as auxiliary signals rather than jointly evolving them with action trajectories. We find that this limitation can be effectively mitigated by a simple yet effective unified condition-action modeling design that represents conditions and actions in a shared token space, allowing a compact model to achieve high performance while improving both inference speed and accuracy. Therefore, we propose UCA-Flow, a unified condition-action modeling framework for accurate one-step action generation. Our method unifies observation conditions, timestep conditions, interval conditions, and action tokens into a single sequence, and processes them with a Unified Condition-Action Transformer for joint condition-action representation learning. As a result, condition representations are dynamically reconstructed according to the current generation stage, highlighting information most relevant for action refinement. Furthermore, we introduce an improved dual-pass supervision scheme over u and v for stronger optimization of unified condition-action modeling. UCA-Flow achieves an average success rate of 86.4%, improving over the strongest baseline by 7.5 percentage points, with a task-averaged inference latency of 3.0 ms. It achieves 43.9× and 32.2× speedups over DP3 and Simple DP3, and remains 4.2× and 2.2× faster than one-step FlowPolicy and MP1, respectively. Project page: https://uca-policy.github.io/UCA.github.io/