This research paper investigates the design and implementation of a Smart Forest Fire Detection System that employs LoRaWAN sensors, machine learning and deep learning algorithms for intelligent prediction in remote forested areas. LoRaWAN sensors are strategically placed to continuously monitor temperature and humidity levels, delivering real-time environmental data. Various machine learning models, deep learning and ensemble learning models like Logistic Regression, MLPClassifier, SVM, CNN, GRU, RNN-LSTM, Adaboost, Bagging, Random Forest, and Max-voting models are trained on past sensor data to forecast forest fire occurrences. This study shows that the CNN-based model yielded an AUC-ROC score of 0.53 and an F1 score of 0.60, the highest among all the algorithms tested. Simulated field tests and evaluations show that the suggested system is effective and reliable for early detection and proactive fire prevention.