Background: Cerebral palsy (CP) is the most prevalent prenatal neurodevelopmental disorder, characterized by a group of permanent movement and postural syndromes resulting from early-stage brain injury. While prevalence varies globally (1.5 to 3.4 per 1,000 live births), the incidence in Mongolia is recorded at 1.89 per 1,000 live births. Although approximately one-third of children with CP are non-ambulatory, those classified within GMFCS Levels I, II, and III retain the ability to walk independently or with assistive devices. Quantitative gait analysis is essential for understanding motor impairments, optimizing treatment planning, and improving clinical prognosis. Aim: To determine the biomechanical characteristics of “Jump gait” in children with cerebral palsy. Materials and Methods: The study included 14 children aged 8–15 diagnosed with CP (GMFCS Level I) and a control group of 14 age-matched healthy children. Anthropometric data (age, sex, weight, height) were collected. Lower limb kinematics and kinetics were measured using a 3D optical motion capture system and force plates. Reflective markers were placed on specific anatomical landmarks to record gait cycles. Results: Comparative kinematic analysis revealed no significant difference in hip joint range of motion (ROM) between the CP and control groups. However, children with CP exhibited a significant reduction in knee ROM, specifically an inability to achieve full knee extension during the gait cycle. Regarding ankle kinematics, children with CP showed negative values (plantarflexion) during the initial heel strike phase, indicating a “Toe-walking” pattern. Statistical significance was observed in ankle ROM across all phases of the gait cycle. No statistically significant difference was found in the vertical ground reaction force (GRF) during the initial contact phase between the two groups. Conclusion The “Jump gait” pattern in children with CP is primarily characterized by restricted knee extension and excessive ankle plantarflexion rather than hip joint dysfunction. Quantifying these biomechanical deviations provides critical data for optimizing personalized rehabilitation and clinical interventions.