Modern soldiers are burdened by an increase in body-borne load due to technological advancements related to their armour and equipment. Despite the potential increase in safety from carrying more protective equipment, a heavier load on the soldier might decrease field performance both cognitively and physically. Additionally, an increasing load on military personnel concurrently increases their risk of musculoskeletal injuries. Therefore, there is a necessity for research on the soldier's biomechanical outcomes under different loading conditions. When it comes to biomechanics research, marker-based technology is widely accepted as the gold standard in terms of motion capture. However, recent advancements in markerless motion capture could allow the quick collection of data in various training environments, while avoiding marker errors. In this research project, the Theia3D markerless motion capture system was compared to the marker-based gold standard for application on participants across varying body-borne load conditions. The aim was to estimate lower body joint kinematics, gastrocnemius lateralis and medialis muscle activation patterns, and lower body joint reaction forces from the two motion capture systems. Data were collected on 16 participants for three repetitions of both walking and running under four body-borne load conditions by both motion capture systems simultaneously. Electromyography (EMG) data of lower limb muscles were collected on the right leg and force plates measured ground reaction forces. A complete musculoskeletal analysis was completed in OpenSim using the Rajagopal full-body model and standard workflow: model scaling, inverse kinematics, residual reduction, static optimization, and joint reaction analysis. Estimations of joint kinematics and joint reaction forces were compared between the two systems using Pearson's correlation coefficient, root-mean-square errors, and Bland-Altman limits of agreement. Very strong correlations (r = 0.960 ± 0.038) and acceptable differences (RMSE = 7.8° ± 2.6°) were observed between the kinematics of the marker-based and markerless systems, with some angle biases due to joint centre differences between systems causing an offset. Because the marker-based motion capture system lost line of sight with markers more frequently in the heavier body-borne load conditions, differences generally increased with heavier body-borne loads. Timing of muscle activations of the gastrocnemius lateralis and medialis as estimated from both systems agreed with the ones measured by the EMG sensors. Joint reaction force results also showed a very strong correlation between the systems but the markerless model seemed to overestimate joint reaction forces when compared to results from the marker-based model. Overall, this research highlighted the potential of markerless motion capture to track participants across all body-borne load conditions. However, more work is necessary on the determination of angle bias between the two systems to improve the use of markerless data with OpenSim models.