Qubit control protocols traditionally characterise qubit-environment coupling using resource-intensive pulse sweeps on quantum hardware. Although previous work attempted to reduce this quantum runtime by inferring noise operators, it relied on a complex grey-box approach combining deep neural networks with physics-encoded layers, posing challenges in training and scaling. Here, we introduce a \textit{quantum feature space} (QFS, denoted $\mathcal{F}$) constructed from a single pulse sequence. By engineering $\mathcal{F}$ to encode qubit-environment dynamics, we eliminate the need for expensive neural networks to learn these physical interactions. This allows us to offload extraction of spectral features to efficient classical machine learning algorithms. Bypassing both the extensive pulse sweeps of traditional quantum noise spectroscopy and the costly neural networks of prior methods, we demonstrate that a simple random forest algorithm on $\mathcal{F}$ classifies broad noise classes with $\sim 90\%$ accuracy under simulated realistic hardware constraints, including pulse jitter and readout errors. Furthermore, Euclidean distance searches over $\mathcal{F}$ can determine spectral resonances. Focusing on noise classification and identification rather than continuous power spectral density reconstruction makes $\mathcal{F}$ an efficient diagnostic tool for quantum hardware.

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