This study applies Routine Activity Theory (RAT) to examine factors influencing victimization in cyber-love scams in Malaysia. With cases rising sharply alongside technological advances, the research sought to identify demographic and behavioral patterns among victims who filed police reports in Selangor. The sample consisted of 280 survey respondents representing a population of 2,508 reported victims. Most participants were women (238), with age groups distributed across under 25, 25–35, 36–45, and 46+. Education levels ranged from diploma to bachelor’s degree, and the majority were employed. Income varied, though both non-income individuals (17%) and those earning above RM2001 were noted as particularly vulnerable. Key variables included cybercrime awareness and computer skill levels. Awareness was measured through knowledge of scams such as phishing, parcel scams, and Nigerian “419” schemes. Skills were assessed by computer usage duration, hours of use, and IT course participation. Reliability testing confirmed strong consistency of instruments, with Cronbach’s alpha values ranging from 0.83 to 0.92. The results showed a strong positive correlation between low levels of cybercrime awareness and the likelihood of victimization (r = 0.626, p < 0.01). A weaker but still significant correlation was found between low computer skills and victimization (r = 0.306, p < 0.01). Demographic findings indicated that individuals aged 25–45 years, those with diplomas, and married people were disproportionately targeted. Victims often either lacked income or were employed with moderate to high earnings, highlighting diverse risk profiles. The study concludes that reduced cybercrime awareness and limited technical skills increase susceptibility to romance scams. It suggests that policymakers and enforcement agencies focus on improving public education, raising digital literacy, and tailoring prevention to high-risk groups. Future research should also analyze the scale of financial losses and consider offender motivations in target selection.