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This study investigates the application of artificial intelligence and smart packaging technologies to enhance the shelf-life of Haldiram's traditional snack products. We developed an integrated system combining IoT-enabled sensor tags (monitoring O₂, CO₂, and humidity levels) with machine learning algorithms (XG Boost and LSTM networks) to predict product freshness in real-time. Accelerated shelf-life testing was conducted on three popular product categories (namkeen, sweets, and fryums) under varying environmental conditions (25-45°C, 60-85% RH). Our results demonstrate that the AI model predicted spoilage events with 92.3% accuracy (F1-score), outperforming conventional expiration dating by 5-7 days. Comparative analysis revealed that bio-based active packaging extended product shelf-life by 34-42% compared to conventional materials, while maintaining sensory qualities (p < 0.05). Consumer acceptance studies (N=512) indicated 73% willingness to pay a 10-15% premium for smart-packaged products. The proposed system shows potential to reduce Haldiram's annual food waste by approximately 28%, translating to estimated savings of ₹110-125 crore. This work presents a scalable framework for implementing Industry 4.0 technologies in traditional food manufacturing, balancing technological innovation with cultural preservation