BACKGROUND: Running biomechanics can influence injury risk, but whether the combined effect of different biomechanical factors can be identified by individual running profiles remains unclear. Here, we identified distinct biomechanical profiles among healthy runners, examined lower limb mechanical load characteristics, and evaluated potential implications for injury risk. HYPOTHESIS: Multiple factors would serve as a common denominator allowing identification of specific patterns. STUDY DESIGN: Cross-sectional. LEVEL OF EVIDENCE: Level 2. METHODS: Step cadence, stance time, vertical oscillation, duty factor, vertical stiffness, peak ground reaction force (GRF), and anteroposterior, lateral, and vertical smoothness were determined from 3-dimensional kinematic data from 79 healthy runners using a treadmill at 2.92 m/s. Principal component analysis, self-organizing maps, and K-means clustering techniques delineated distinct biomechanical running profiles. Mutual information analysis, Kruskal-Wallis, and Pearson's Chi-squared tests were conducted. RESULTS: Five biomechanical profiles (P1-P5) demonstrated different running mechanical characteristics: P1 exhibited low cumulative and peak mechanical load due to a combination of high duty factor, low step cadence, and longer stance time; P2 showed characteristics associated with the lowest peak mechanical load due to reduced peak GRF and greater smoothness; P3 and P5 showed contrasting running patterns, but maintained moderate smoothness and peak GRF; and P4 exhibited the highest peak mechanical load, driven by high GRF, low duty factor, and high vertical oscillation. CONCLUSION: The 5 profiles appear to be associated with different lower limb load patterns, highlighting previously unrecognized connections between biomechanical variables during running. Some variables contribute to increased peak and cumulative load, whereas others help reduce it, underscoring the complex interplay of biomechanical factors in running. CLINICAL RELEVANCE: Identifying distinct running profiles can help clinicians better understand individual variations in mechanical load and injury risk, thus informing targeted interventions, such as personalized training adjustments or rehabilitation programs, to prevent injuries and enhance performance in runners.