With the expanding reliance on renewable energy technologies specifically solar photovoltaic (PV) systems and battery energy storage, households can generate, store, and trade their surplus energy in decentralized peer-to-peer (P2P) markets. The challenge lies in dynamically managing energy consumption by deciding when to utilize solar power, battery storage, or purchase energy from the grid or peer markets, based on fluctuating costs and availability. This research proposes an intelligent load demand allocation model based on a segmented Markov chain approach, aimed at enhancing energy distribution in a P2P energy trading network. The study introduces a Markov chain-based model that allocates energy demand from these sources efficiently by predicting the most cost-effective energy mode at any given time. The model incorporates multiple time segments throughout the day, adjusting the transition probabilities between each energy source based on varying energy consumption patterns and external factors. Results show that the proposed Markov model significantly enhances decision-making in smart household energy systems, optimizing energy sharing while minimizing costs. The simulations demonstrate how the model adapts to fluctuating solar generation and peer market prices, ensuring efficient load management. This research helps in creating energy systems that are both sustainable and cost-effective, supporting efficient P2P energy trading and contributing to a future with more decentralized and renewable energy. Future work will expand the research to develop market rules and utilize genetic algorithms to further optimize battery capacities for prosumers, maximizing benefits while considering individual constraints.