Personalized education requires learning path recommendations that adapt to individual performance while satisfying prerequisite constraints. However, generating optimal learning sequences presents significant computational challenges due to the combinatorial complexity of curriculum graphs, heterogeneous learner profiles, and directed prerequisite dependencies that constrain valid traversal orders. This paper proposes a Hybrid Swarm Intelligence Framework combining Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC) for adaptive learning path recommendation. The hybrid architecture addresses distinct subproblems: PSO optimizes continuous feature weights and algorithm parameters, ACO constructs prerequisite-feasible paths through pheromone-guided graph traversal, and ABC refines solutions through local neighborhood exploration to escape suboptimal configurations. The curriculum is modeled as a directed weighted graph where nodes represent courses with learner-specific difficulty scores derived from behavioral features. Student behavioral data from 8,783 learners (6,968,707 records) inform learner-specific heuristics. The framework operates as a two-stage process: first, linear regression predicts GPA from behavioral features; second, the swarm intelligence pipeline selects paths maximizing predicted academic success. Experimental evaluation demonstrates a +20.69% improvement in mean path fitness scores compared to the Ridge baseline (p < 0.001, paired Cohen's dz = 3.48). We compared the framework against Deep Learning baselines (LSTM and GRU) configured as sequence-aware grade regressors. While the optimally tuned GRU emerged as a strong predictor (+11.88% over baseline), it fell significantly short of the Hybrid Swarm. Results suggest that for curriculum sequencing under prerequisite constraints, combinatorial swarm optimization can outperform greedy decoding from neural predictors when training data is limited. By demonstrating that swarm intelligence algorithms can be systematically combined to address prerequisite satisfaction and behavioral personalization, this work offers a scalable, interpretable alternative to opaque deep learning approaches.