Organisations around the world face never-before-seen difficulties as a result of the quickly changing cybersecurity landscape, which calls for advanced analytical techniques to recognise and anticipate threat trends. In order to analyse current cyberthreats and their organisational impact, this study uses sophisticated statistical techniques such as ANOVA, regression analysis, machine learning classification, and clustering algorithms. We examine threat distribution patterns, financial impact correlations, and predictive modeling capabilities for ten key threat categories malware, phishing, social engineering, ransomware, insider threats, supply chain attacks, DoS/DDoS attacks, zero-day exploits, credential theft, and AI-powered attacks by thoroughly analysing 360 cyber security incidents that occurred across several Indian cities between 2019 and 2024. The most significant predictor of financial impact, according to our statistical analysis, is incident type (F = 4.58, p < 0.001), with phishing attacks accounting for 60% of recorded incidents. Clustering analysis revealed clear threat patterns with a silhouette score of 0.297, while advanced classification models performed moderately (accuracy = 11%, cross-validation score = 0.1033). Strong predictive ability was shown by regression modeling for financial impact assessment (R2 = 1.00, MSE = 155,144.37). The results demonstrate the usefulness of statistical methods in threat intelligence and risk assessment frameworks and offer empirical support for data-driven cybersecurity decision-making.