This paper presents a novel hybrid tribioinspired model for feature selection that employs three algorithms for feature selection: Whale Optimization for Interclass Variance Maximization, Particle Swarm Optimization for Intraclass Variance Minimization, and Firefly Optimization for Best Weights Selection. The WOICVM algorithm is used as this contains an excellent exploration-exploitation balance for maximizing the interclass variance to induce significant separability among classes. PSOICVM is highly efficient in large-scale optimization, minimizes intraclass variance, and improves cohesiveness within each class. Finally, the Firefly Algorithm optimally combines WOICVM's and PSOICVM's strengths by determining the best weighting scheme and balancing interclass and intraclass variances. This multiobjective approach enhances feature selection efficiency by leveraging the complementary advantages of the three algorithms. Tentative numerical results depict a 15% increase in inter-class variance with WOICVM, a 12% reduction in intraclass variance with PSOICVM, and a 20% improvement in overall feature selection efficiency through FOBWS. This thereby shows a 10% enhancement in classification accuracy in high-dimensional environments, showing the efficiency of the proposed model over conventional methods. It fills critical gaps in existing methods by offering a hybrid method as a strong tool for applying big data to improve classification performance.