Data integrity remains a critical determinant of analytical reliability, regulatory compliance, and pharmaceutical product quality, particularly where laboratory operations depend on interconnected analytical instruments, computerized systems, and controlled procedural modifications. Conventional pharmaceutical quality management approaches frequently evaluate change control, instrument qualification, and computer system validation independently, creating fragmented risk assessments, incomplete traceability, and delayed identification of data integrity vulnerabilities. This study proposes an integrated computational framework incorporating these three quality assurance functions to strengthen data integrity throughout the pharmaceutical analytical lifecycle. A novel Change Control–Instrument Qualification–Computer System Validation Data Integrity Optimization Algorithm (CICV-DIOA) is developed to integrate multidimensional risk assessment, qualification status monitoring, validation traceability, audit-trail anomaly detection, and adaptive corrective action prioritization. The proposed algorithm combines dynamically weighted risk indicators, Bayesian probability updating, temporal anomaly detection, and constrained multi-objective optimization to estimate data integrity vulnerabilities and recommend preventive interventions. The framework is designed for pharmaceutical quality control environments involving high-performance liquid chromatography, gas chromatography, ultraviolet-visible spectrophotometry, laboratory information management systems, and chromatography data systems. Its performance will be evaluated using historical laboratory quality records and controlled simulation scenarios incorporating unauthorized data modifications, calibration deviations, incomplete validation documentation, audit-trail inconsistencies, and inadequately controlled system changes. CICV-DIOA will be benchmarked against Failure Mode and Effects Analysis, Fuzzy-FMEA, Random Forest, and Extreme Gradient Boosting using comparable risk-ranking and predictive evaluation protocols. Performance indicators will include anomaly detection accuracy, precision, recall, F1-score, false-positive rate, risk prioritization effectiveness, computational execution time, and residual data integrity risk. Comparative graphs, risk distribution heatmaps, convergence profiles, and sensitivity analyses will be employed to examine algorithmic stability, computational efficiency, and robustness under varying laboratory operating conditions. The anticipated contribution is an integrated, risk-responsive decision-support framework capable of improving early vulnerability identification, strengthening change-to-validation traceability, and supporting evidence-based allocation of laboratory quality assurance resources. The proposed framework is intended to advance pharmaceutical data integrity management through coordinated lifecycle controls and systematic evaluation of analytical and computerized system risks.