The global pandemic situation has severely affected all countries. As a result, almost all countries had to adjust to online technologies to continue their processes. In addition, Sri Lanka is yearly spending ten billion on elections. We have examined a proper way of minimizing the cost of hosting these events online. To solve the existing problems and increase the time potency and cost reduction we have used IoT and ML-based technologies. IoT-based data will identify, register, and be used to secure from fraud, while ML algorithms manipulate the election data and produce winning predictions, weather-based voters attendance, and election violence. All the data will be saved in cloud computing and a standard database to store and access the data. This study mainly focuses on four aspects of an E-voting system. The most frequent problems across the world in E-voting are the security, accuracy, and reliability of the systems. E-government systems must be secured against various cyber-attacks and ensure that only authorized users can access valuable, and sometimes sensitive information. Being able to access a system without passwords but using biometric details has been there for a while now, however, our proposed system has a different approach to taking the credentials, processing, and combining the images, reformatting and producing the output, and tracking. In addition, we ensure to enhance e-voting safety. While ML-based algorithms use different data sets and provide predictions in advance.

翻译：全球疫情形势严重影响了所有国家。为此，几乎所有国家都不得不转向在线技术以维持其流程运转。此外，斯里兰卡每年在选举上耗费百亿卢比。我们研究了将此类活动在线举办以降低成本的合理方式。为解决现有问题并提升时效与降低成本，我们采用了基于物联网和机器学习的技术。基于物联网的数据将用于识别、注册并防范欺诈，而机器学习算法则用于处理选举数据、生成获胜预测、基于天气的选民投票率预测以及选举暴力预测。所有数据将保存于云计算平台与标准数据库中，以便存储与访问。本研究主要聚焦电子投票系统的四个方面。全球电子投票中最常见的问题是系统的安全性、准确性和可靠性。电子政务系统必须能抵御各类网络攻击，确保只有授权用户能访问重要乃至敏感的信息。虽然无需密码而通过生物特征访问系统的方式已存在一段时间，但我们提出的系统采用不同方法来获取凭证、处理并合成图像、重新格式化并生成输出结果以及追踪。此外，我们确保增强电子投票的安全性。而基于机器学习的算法则利用不同数据集提前进行预测。