<p>Internet of Things (IoT) services have developed rapidly in recent years. However, existing research has not yet proposed a mature method for quantitatively evaluating IoT services, nor has it correctly considered the impact of cooperation and competition among IoT service providers on IoT service pricing strategies. This paper constructs a duopolistic Cournot competition model comprising data providers, service providers, and consumers. IoT service providers can utilize machine learning to train models that generate user-oriented IoT services, then market and monetize them, thereby generating profits. This paper introduces a logistic-curve-based service quality function, using the amount of data employed for machine learning as the principal metric of IoT service quality. We then describe the competitive behavior between IoT service providers in a Cournot duopoly market and derive the IoT service pricing and machine learning training data procurement strategies that maximize profits for both parties. Subsequently, we explore the possibility of bundling IoT services at discounted prices and categorize the scenarios into four cases based on the relationship between service quality and pricing. We calculate the pricing of the IoT service bundle and the optimal data quantities for both parties to maximize their profits. Finally, we incorporate a price fluctuation factor to account for market uncertainty, refining the profit function model to better reflect actual market dynamics.</p>