Technology-facilitated intimate partner violence (TF-IPV) is an emerging form of abuse wherein digital and modern technologies are used to control, monitor, intimidate, humiliate, or harass a current or former partner. The rising prevalence of TF-IPV, coupled with its physical, psychological, and economic impacts on victims, necessitates urgent attention. However, research in this area is nascent, especially in examining associated individual factors. This study employed a person-centered approach to identify distinct latent profiles based on dark personality traits, emotion regulation difficulties, self-compassion, and conflict resolution styles, and examined their relationship to TF-IPV victimization outcomes. A sample of 221 Indian adults (≥ 18 years) in intimate relationships completed validated measures of dark personality traits, emotion regulation difficulties, self-compassion, conflict resolution styles, and TF-IPV victimization experiences. Latent profile analysis was conducted to identify different subgroups, followed by non-parametric tests to examine profile differences in TF-IPV outcomes. Three distinct profiles emerged: Adaptive (14.9%; characterized by low dark traits, effective emotion regulation, high self-compassion, and high positive problem solving), Normative (71.0%; near-average levels across all variables), and High-Risk (14.0%; elevated dark traits, emotion regulation difficulties, low self-compassion, and high conflict engagement and withdrawal). Significant differences were found across profiles for monitoring/control behaviors (χ²(2) = 10.384, p = .006), humiliation (χ²(2) = 6.017, p = .049), threats (χ²(2) = 7.904, p = .019), and total TF-IPV scores (χ²(2) = 13.959, p = .001). The High-Risk profile demonstrated the highest levels of TF-IPV victimization across all dimensions, with effect sizes ranging from small to medium (r = .145 to .240). The findings reveal meaningful heterogeneity in individual vulnerability to TF-IPV victimization, suggesting that specific combinations of personality traits, emotion regulation capacities, and protective factors create differential risk. Results support developing targeted interventions based on individual risk and protective factor profiles.