Background: Gram-negative infections and multidrug resistance (MDR) constitute a substantial and escalating threat in critically ill patients, contributing to increased morbidity, mortality, and healthcare burden. The emergence of resistant pathogens in intensive care units (ICUs) is driven by multiple factors, including excessive antimicrobial exposure, prolonged hospitalization, invasive procedures, and underlying comorbidities. Early identification of patients at risk for MDR infections is essential for optimizing empirical therapy and strengthening antimicrobial stewardship strategies. Objectives: To identify predictors of Gram-negative infections and determine independent risk factors associated with multidrug resistance among critically ill patients using a predictive modelling approach. Methods: A prospective observational predictive modelling study was conducted among 100 critically ill patients admitted to a tertiary care ICU. Demographic, clinical, and microbiological variables including age, sex, ICU stay, antibiotic burden, comorbidities, infection source, and organism type were analysed. Univariate associations were assessed using Chi-square testing, and independent predictors were identified through multivariable logistic regression analysis. Statistical significance was defined as p<0.05. Results: Gram-negative organisms predominated (79.1%), and the overall prevalence of multidrug resistance was 42%. Univariate analysis demonstrated significant associations between MDR and prolonged ICU stay (p=0.012), ≥5 antibiotic exposure (p=0.009), resistant isolate burden (p=0.004), and urinary source infections (p=0.031). Multivariable logistic regression identified ≥5 antibiotics (aOR 3.41; 95% CI 1.44–8.06; p=0.005), ICU stay >8 days (aOR 2.89; 95% CI 1.19–7.03; p=0.019), Gram-negative isolate (aOR 3.27; 95% CI 1.22–8.77; p=0.018), and diabetes mellitus (aOR 2.21; 95% CI 1.01–4.84; p=0.046) as independent predictors of MDR. Conclusion: Poly-antibiotic exposure, prolonged ICU stay, Gram-negative infections, and diabetes mellitus were identified as significant independent determinants of multidrug resistance. Predictive risk stratification models based on these variables may enhance empirical antimicrobial decision-making and support targeted stewardship interventions in critical care settings.