Security measures are vital in the present day and age, especially in regions such as Kerala, India, where the use of the internet and digital technology is rapidly growing hand in hand with new threats. This work therefore sought to establish the participants’ levels of awareness and experiences with cybercrime in Kerala through machine learning models. The research is based on the survey data of users demography (age, gender, education, urban environment/rural area) awareness scores, and personal experience of cybercrimes (phishing, malware attacks, identity theft, and online fraud). This entails creating and distributing a questionnaire to obtain the required data; the next steps include data cleaning aspects such as missing data imputation, creating dummy variables for the categorical variables, and scaling for numerical ones. To make predictions from the given data, the Decision Trees, Random Forests, Logistic Regression, K-means Clustering, and Support Vector Machines algorithms are used. These algorithms are chosen because they can effectively distinguish cybersecurity events and identify factors that might influence people to become cybercrime victims. Important factors to be noted are the correlation between the demographic data and the existing level of awareness regarding cybersecurity, the frequency rates for several types of cybercrimes, and the evaluation of the extant models of cybersecurity in the state of Kerala in India. As found, to overcome the issue, it is necessary to launch appropriate awareness programs and implement greatly needed cybersecurity initiatives, which should always be adapted to the demographics of the regions with high cyber risks and the ways people utilize digital technologies. Hence, the study provides government policymakers, cyber-security workers, and other interested researchers a better understanding about cybersecurity status and future prospects of Kerala, India, and other similar digital societies.