Gait impairment post-stroke is highly heterogeneous. Prior studies classified heterogeneous gait patterns into subgroups using peak kinematics, kinetics, or spatiotemporal variables. A limitation of this approach is the need to select discrete features in the gait cycle. Using continuous gait cycle data, we accounted for differences in magnitude and timing of kinematics. Here, we propose a machine-learning pipeline combining supervised and unsupervised learning. We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. Then, we used unsupervised time-series k-means and Gaussian Mixture Models to identify gait clusters. We tested our pipeline using kinematic data of 28 neurotypical and 39 individuals post-stroke. We assessed differences between clusters using ANOVA. We identified two neurotypical gait clusters (C1, C2). C1: normative gait pattern. C2: shorter stride time. We observed three post-stroke gait clusters (S1, S2, S3). S1: mild impairment and increased bilateral knee flexion during loading response. S2: moderate impairment, slow speed, short steps, increased knee flexion during stance bilaterally, and reduced paretic knee flexion during swing. S3: mild impairment, asymmetric swing time, increased ankle abduction during the gait cycle, and reduced dorsiflexion bilaterally. Our results indicate that joint kinematics post-stroke are mostly distinct from controls, and highlight kinematic impairments in the non-paretic limb. The post-stroke clusters showed distinct impairments that would require different interventions, providing additional information for clinicians about rehabilitation targets.