Non-intrusive load monitoring (NILM) aims to estimate the operational states and power consumption of individual household appliances, providing real-time insights into energy usage for effective energy management and improved demand side response strategies. This study addresses the challenge of accurate energy disaggregation of household energy consumption data into individual appliances’ consumption, an important requirement for effective energy management in smart homes. Traditional energy monitoring systems provide only aggregate data, limiting the ability to optimize energy consumption. To overcome these difficulties, this study proposes a Convolutional Neural Network (CNN)-based model for Non-Intrusive Load Monitoring (NILM) that disaggregates total energy usage into appliance-specific consumption for five key appliances: kettle, microwave, fridge, dishwasher, and washing machine. Unlike the previous approaches, our model integrates a hybrid dataset from UK-DALE and REFIT, leveraging data fusion techniques to enhance generalization. The CNN architecture employed uses five convolutional layers for effective feature extraction, capturing temporal dependencies in appliance usage patterns and thus results in an improved MAE and SAE when compared to similar published results. The preprocessing and hybridization stage involves such processes as missing data imputation, appliance state labelling, feature normalization and merging of the datasets. The developed model achieved an overall accuracy of 98.3% and an F1-score of 81.7% in seen scenarios, while in unseen environments, it attained 96.5% accuracy and an F1-score of 58.1% when tested on the UK-DALE dataset. The seen scenario refers to testing using UK-DALE House 1 and REFIT House 2 data of the validation dataset, whereas the unseen scenario involves entirely new house data not used during training and validation.  It is shown that post-processing techniques reduce errors, highlighting its effectiveness, which help to enhance the model's predictive accuracy. This study contributes to the advancement of NILM technologies by combining datasets, offering a robust and scalable solution for individual appliace energy monitoring, with significant implications on energy conservation and smart home efficiency.