This paper looks into the impact of machine learning models in predicting bitcoin prices, with a focus on the fintech environment that has been shaped by Amazon India. It explores how digital investors’ behavior, the use of payment gateways, and the expansion of e-commerce affect the volatility of the biggest cryptocurrencies. Sophisticated algorithms, such as Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks, were applied to sentiment-inputted time series data. The paper assessed these models’ anticipated accuracy, precision, and consistency in order to determine their practical usefulness. The prediction power of the models was increased by including sentiment analysis from Amazon India’s consumer interaction and payment trends. The paper shows the efficacy of hybrid tactics that combine financial data with social and technological indicators. Machine learning is becoming increasingly important for investors, fintech companies, and dealers in India’s growing digital economy.